关键词分类
高级
这是一个自动化工作流，包含 40 个节点。主要使用 Set, Filter, Airtable, SplitOut, Aggregate 等节点。 使用AI和Airtable分类SEO关键词并创建内容策略
前置要求
- •Airtable API Key
- •OpenAI API Key
分类
-
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "mGgSDkJTDBI4mq1J",
"meta": {
"instanceId": "642e489d8be5ef18e94f4513d3dbcdb97cfdc6b36fc67668bf60c866346c5a09",
"templateCredsSetupCompleted": true
},
"name": "关键词分类",
"tags": [
{
"id": "AZvUGDdqsfK0AaPB",
"name": "Content Creation",
"createdAt": "2025-09-09T14:22:23.903Z",
"updatedAt": "2025-09-09T14:22:23.903Z"
}
],
"nodes": [
{
"id": "478452bb-0bf2-46c0-8644-d4f9bd2e31c7",
"name": "点击“测试工作流”时",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-1024,
864
],
"parameters": {},
"typeVersion": 1
},
{
"id": "95bd2d16-c8eb-4841-8a89-61f60bb111ac",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
48,
224
],
"parameters": {
"width": 520,
"height": 540,
"content": "## 从主列表获取关键词并进行分类"
},
"typeVersion": 1
},
{
"id": "c222ee7d-0791-40ad-9780-e3d067d01ffa",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
848,
144
],
"parameters": {
"width": 520,
"height": 260,
"content": "## 将所有分类后的关键词发送至 Airtable"
},
"typeVersion": 1
},
{
"id": "b99967c3-a895-47bf-9b57-cbdde4f3394b",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
640,
448
],
"parameters": {
"width": 1720,
"height": 460,
"content": "## 为每个分类关键词创建标题和描述"
},
"typeVersion": 1
},
{
"id": "4d98bb25-96b6-4e2c-8e1d-1b9929259ea5",
"name": "智能体创建内容机会",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1440,
1504
],
"parameters": {
"text": "=For each of the {{ $('Edit Fields2').item.json.number_of_clusters }} clusters, consider its:\n- cluster_name: {{ $json.cluster_name }}\n- core_topic: {{ $json.core_topic }}\n- intent_pattern:{{ $json.intent_pattern }}\n- keywords: {{ $json.keywords }}\n- reasoning: {{ $json.reasoning }}\n- primary_keyword: {{ $json.primary_keyword }}\nTo create one hub article and 5 spoke articles.\n\nYou are creating a hub and spoke content strategy. For each hub and spoke article, you will create:\n- title: an seo-optimized title that is engaging and reflects the article content. It is around 60 characters long.\n- description: 3 to 5 paragraphs desribing the article\n- keyword: the primary keyword of the article\n\nTo create the clusters group all keywords with the same cluster_name. Give each cluster a name and type of either hub or spoke. There will be one hub article and up to 5 spoke articles per cluster.\n\nThe hub article serves as the central piece of content that provides a broad overview of a main topic. The spoke articles are detailed content pieces that branch out from the hub, each focusing on specific subtopics.\n\nImportant: Each cluster as defined above should have one hub article and up to 5 spoke articles. Always make sure that hub and spoke articles are created for each cluster.\nConsider the following when creating the hub and spoke articles.\n1. cluster_name\n2. title\n- SEO-optimized\n- Under 62 characters\n3. description\n- 3-5 paragraphs desribing the article\n4. type \n- hub or spoke\n\nReturn a single-line JSON object with this structure for each cluster (no line breaks). \n {\n \"cluster_name\": \"descriptive name\",\n \"title\": \"the title\",\n \"description\": \"the description\",\n \"type\": \"either hub or spoke\",\n \"keyword\": \"the primary keyword\"\n \"reasoning\": \"reasoning for the cluster\"\n }\n\nImportant: Return ONLY the JSON array with NO line breaks (\\n), NO extra quotations, NO extra spaces, and NO additional text.\n\n\n\n",
"options": {
"systemMessage": "You are an AI content strategist specializing in creating seo-optimized hub and spoke content structures. For each cluster, create one main hub article and up to 5 supporting spoke articles that comprehensively cover the topic while maintaining SEO best practices and user intent.\n"
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "88679472-31f9-499f-86f0-7b06fa058f0b",
"name": "便签4",
"type": "n8n-nodes-base.stickyNote",
"position": [
32,
1072
],
"parameters": {
"width": 800,
"height": 520,
"content": "## 从主关键词全变体列表中聚类关键词"
},
"typeVersion": 1
},
{
"id": "b482852c-8502-4b0d-b42e-819a1cb02e9a",
"name": "便签5",
"type": "n8n-nodes-base.stickyNote",
"position": [
880,
1072
],
"parameters": {
"width": 1120,
"height": 260,
"content": "## 将聚类和关键词添加至聚类表格"
},
"typeVersion": 1
},
{
"id": "1bb158eb-5092-4aa9-be97-98c3fd8a4125",
"name": "便签6",
"type": "n8n-nodes-base.stickyNote",
"position": [
880,
1360
],
"parameters": {
"width": 1600,
"height": 460,
"content": "## 创建中心辐射式内容机会"
},
"typeVersion": 1
},
{
"id": "a52ef80c-284c-491b-a4c5-509748fffd10",
"name": "便签7",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
0
],
"parameters": {
"color": 3,
"width": 2544,
"height": 920,
"content": "## 分类并创建内容机会"
},
"typeVersion": 1
},
{
"id": "656ea262-9516-4ef9-9ac7-3748d18d0ae9",
"name": "便签8",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
960
],
"parameters": {
"color": 3,
"width": 2540,
"height": 920,
"content": "## 聚类并创建内容机会"
},
"typeVersion": 1
},
{
"id": "85391b1c-4813-4466-a710-4055f8788421",
"name": "过滤未知类别",
"type": "n8n-nodes-base.filter",
"position": [
880,
560
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "fa613938-57a8-49f6-a01b-723028c6cbf3",
"operator": {
"type": "string",
"operation": "notEquals"
},
"leftValue": "={{ $json.fields.Category }}",
"rightValue": "Unknown"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "7ad32cab-aa16-4f85-ac97-9b4c395ef649",
"name": "设置分类表字段",
"type": "n8n-nodes-base.set",
"position": [
944,
240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "3f1f38b9-8941-4047-8904-270b17bfc2ed",
"name": "keyword",
"type": "string",
"value": "={{ JSON.parse($json.output).keyword }}"
},
{
"id": "849f572c-1339-4a83-bd4c-2b4c13cd80d4",
"name": "category",
"type": "string",
"value": "={{ JSON.parse($json.output).category }}"
},
{
"id": "5909d7fe-6050-490d-bf90-ad0817d882a0",
"name": "reasoning",
"type": "string",
"value": "={{ JSON.parse($json.output).reasoning }}"
},
{
"id": "aeb81379-fba7-483d-828c-427f2851ee87",
"name": "msv",
"type": "number",
"value": "={{ JSON.parse($json.output).msv }}"
},
{
"id": "3b7b8188-de51-4a6b-95cb-73a8de200996",
"name": "kw_difficulty",
"type": "number",
"value": "={{ JSON.parse($json.output).kw_difficulty }}"
},
{
"id": "013f802d-6c2e-4519-8104-5d3951f845cd",
"name": "search_intent",
"type": "string",
"value": "={{ JSON.parse($json.output).search_intent }}"
},
{
"id": "a2799b9e-8c28-4c02-8162-2874cae2bf2b",
"name": "competition",
"type": "string",
"value": "={{ JSON.parse($json.output).competition }}"
},
{
"id": "7929eaff-788c-43fa-899f-cd04953f8f7f",
"name": "cpc",
"type": "number",
"value": "={{ JSON.parse($json.output).cpc }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "43bb97fe-3742-4f59-a010-6f62e78015f7",
"name": "分类表",
"type": "n8n-nodes-base.airtable",
"position": [
1168,
240
],
"parameters": {
"base": {
"__rl": true,
"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.airtable_base_id }}"
},
"table": {
"__rl": true,
"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.categories_table_id }}"
},
"columns": {
"value": {
"CPC": "={{ $json.cpc }}",
"MSV": "={{ $json.msv }}",
"Keyword": "={{ $json.keyword }}",
"Category": "={{ $json.category }}",
"Reasoning": "={{ $json.reasoning }}",
"Competition": "={{ $json.competition }}",
"KW Difficulty": "={{ $json.kw_difficulty }}",
"Search Intent": "={{ $json.search_intent }}"
},
"schema": [
{
"id": "Category",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Category",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Keyword",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Reasoning",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Reasoning",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "MSV",
"type": "number",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "MSV",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "KW Difficulty",
"type": "number",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "KW Difficulty",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Search Intent",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Search Intent",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Competition",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Competition",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "CPC",
"type": "number",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "CPC",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "create"
},
"credentials": {
"airtableTokenApi": {
"id": "IdMuG6Ja3xc0kVnw",
"name": "Airtable Personal Access Token account"
}
},
"typeVersion": 2.1
},
{
"id": "e20decb0-fd75-4cd4-8b4c-3143d2dd9217",
"name": "设置分类内容字段",
"type": "n8n-nodes-base.set",
"position": [
1104,
560
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "cae1e309-cab6-472a-9bfc-75448a504455",
"name": "keyword",
"type": "string",
"value": "={{ $json.fields.Keyword }}"
},
{
"id": "1c725316-fa66-401c-b028-6039c7845540",
"name": "category",
"type": "string",
"value": "={{ $json.fields.Category }}"
},
{
"id": "328a070b-312e-4c39-bacb-970d0e485e7a",
"name": "reasoning",
"type": "string",
"value": "={{ $json.fields.Reasoning }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "852c9c71-f2bd-4aa6-abed-5f6a4aee633d",
"name": "分类AI智能体",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
192,
368
],
"parameters": {
"text": "=Please categorize the following keywords according to the rules. Ensure every keyword is processed and included in the output.\n\nKeyword to Categorize and Data for Categorization:\n {{ $json.keyword }}\n{{ $json.msv }}\n{{ $json.search_intent }}\n{{ $json.kw_difficulty }}\n{{ $json.competition }}\n{{ $json.cpc }}\n",
"options": {
"systemMessage": "You are an AI agent specialized in analyzing and categorizing SEO keywords and search intent signals.\n\nCategorization Rules:\n1. Quick Wins:\n - MSV > 100 AND KW Difficulty < 30\n - These are opportunities for relatively fast ranking\n\n2. Authority Builders:\n - KW Difficulty > 50 AND MSV > 200\n - High-value terms worth investing in for authority building\n\n3. Emerging Topics:\n - MSV < 100 OR (doesn't fit Quick Wins AND shows future potential)\n - Focus on search intent and growth potential\n- Represent trends or novel concepts that are likely to grow in popularity\n\n4. Intent Signals:\n - People Also Ask questions\n - Direct user questions that show search intent\n - Good opportunities for featured snippets and AI results\n - Categorize here even if metrics are missing\n\n5. Semantic Topics:\n - Autocomplete suggestions and semantic subtopics\n - Related concepts that build topic authority\n - Categorize here even if metrics are missing\n\n6. Unknown:\n - Keywords that don't fit other categories\n - Or have insufficient data and aren't questions/semantic topics\n\nReturn only a JSON object for each keyword with these exact fields:\n{\n \"keyword\": \"exact keyword text\",\n \"category\": \"Quick Wins|Authority Builders|Emerging Topics|Intent Signals|Semantic Topics|Unknown\",\n \"reasoning\": \"brief explanation of categorization\",\n \"msv\": number or null,\n \"kw_difficulty\": number or null,\n \"search_intent\": \"original intent or null\",\n \"competition\": \"original competition or null\",\n \"cpc\": number or null\n}\n\n\nImportant: Return ONLY the JSON object with NO line breaks (\\n), NO extra spaces, and NO additional text.\n\nImportant:\n- Process EVERY keyword in the input\n- Preserve all original data\n- Ensure total_processed matches input count\n- Provide brief reasoning for each categorization\n- please be accurate with the language, if the researches are made in Dutch, write in Dutch"
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "61240f06-f973-49de-9327-08130f0a3b39",
"name": "分类AI智能体的内容创意",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1584,
640
],
"parameters": {
"text": "=For each keyword, analyze its category and reasoning to create:\n\n1. A title that:\n - Captures search intent\n - Is engaging and clickable\n - Includes the main keyword naturally\n - Is optimized for both search and social sharing\n - Is under 60 characters\n - Aligns with the category context: {{ $json.category }}\n \n2. A description that:\n - Clearly outlines the main topics and key points the article should cover\n - Indicates specific value readers will gain\n - Provides enough context to guide content creation\n - Naturally incorporates the keyword\n - Is concise but comprehensive (aim for 2-3 sentences)\n - Considers the categorization reasoning: {{ $json.reasoning }}\n\nInput:\n\n{{ $json.keyword }}\n\nReturn a single-line JSON object with this structure (no line breaks):\n{\"keyword\":\"input keyword\",\"title\":\"created title\",\"description\":\"article description\"}",
"options": {
"systemMessage": "You are an AI content strategist specialized in creating engaging, SEO-optimized titles and article descriptions."
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "ccf02d2e-596b-47fa-bea5-9f5ba666c054",
"name": "设置工作流字段",
"type": "n8n-nodes-base.set",
"position": [
-208,
864
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "acee4494-86dc-4f48-b5d8-e18e7db9eaaa",
"name": "keyword",
"type": "string",
"value": "={{ $json.Keyword }}"
},
{
"id": "575ec827-4dd3-4d4c-8aca-14c80f4a9015",
"name": "record_id",
"type": "string",
"value": "={{ $json.id }}"
},
{
"id": "b5d76b17-8473-4369-9c50-7c0194bce34e",
"name": "primary_keyword",
"type": "string",
"value": "={{ $json['Primary Keyword'] }}"
},
{
"id": "8e4e71d4-662f-4c59-9f01-5b0f14ffb54f",
"name": "competition",
"type": "string",
"value": "={{ $json.Competition }}"
},
{
"id": "5925b3bb-8581-4dfa-9aec-b3578cabef35",
"name": "msv",
"type": "number",
"value": "={{ $json.MSV }}"
},
{
"id": "4de58314-1701-4a95-9826-f28233c1a87d",
"name": "search_intent",
"type": "string",
"value": "={{ $json['Search Intent'] }}"
},
{
"id": "dfb906d8-3c2e-446e-bee3-d24ba4610927",
"name": "kw_difficulty",
"type": "number",
"value": "={{ $json['KW Difficulty'] }}"
},
{
"id": "2fdf1cd1-742b-418b-8966-33823c5aa90b",
"name": "type",
"type": "string",
"value": "={{ $json.Type }}"
},
{
"id": "89472023-1df7-4d64-b874-9106dee97866",
"name": "cpc",
"type": "number",
"value": "={{ $json.CPC }}"
},
{
"id": "9fdccded-22de-4daf-86bb-67ae1db5bfec",
"name": "Date Pulled from D4SEO",
"type": "string",
"value": "={{ $json['Date Pulled from D4SEO'] }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "9eb70197-dc70-4696-8c0f-3175200d2bc3",
"name": "设置内容创意表字段",
"type": "n8n-nodes-base.set",
"position": [
1952,
640
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "3b11eddb-dfa2-4e28-a4e5-8cf3996e3e56",
"name": "keyword",
"type": "string",
"value": "={{ JSON.parse($json.output).keyword }}"
},
{
"id": "eaa730fd-678f-42fb-95d7-fb0210fefcd7",
"name": "title",
"type": "string",
"value": "={{ JSON.parse($json.output).title }}"
},
{
"id": "eb7f4d7d-c669-4f13-acfc-109e6af78f96",
"name": "description",
"type": "string",
"value": "={{ JSON.parse($json.output).description }}"
},
{
"id": "76254565-f080-41d6-b8ed-e71d22c315da",
"name": "primary_keyword",
"type": "string",
"value": "={{ $('Set WF Fields').item.json.primary_keyword }}"
},
{
"id": "619a217e-8526-499d-826a-8874364f0b3b",
"name": "reasoning",
"type": "string",
"value": "={{ $('Filter Out Unknown').item.json.fields.Reasoning }}"
},
{
"id": "74a4cb6e-8ca6-4ebe-b821-1b26d018d1b8",
"name": "category",
"type": "string",
"value": "={{ $('Filter Out Unknown').item.json.fields.Category }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "27cbaea3-5b5d-4c2c-b687-3f0784aec8a7",
"name": "分类内容创意表",
"type": "n8n-nodes-base.airtable",
"position": [
2160,
640
],
"parameters": {
"base": {
"__rl": true,
"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.airtable_base_id }}"
},
"table": {
"__rl": true,
"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.content_ideas_from_kws_table_id }}"
},
"columns": {
"value": {
"Title": "={{ $json.title }}",
"Status": "Not Started",
"Keyword": "={{ $json.keyword }}",
"Category": "={{ $json.category }}",
"Reasoning": "={{ $json.reasoning }}",
"Description": "={{ $json.description }}",
"Primary Keyword": "={{ $json.primary_keyword }}"
},
"schema": [
{
"id": "Title",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Title",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Status",
"type": "options",
"display": true,
"options": [
{
"name": "Not Started",
"value": "Not Started"
},
{
"name": "Send to Article Writer",
"value": "Send to Article Writer"
},
{
"name": "Discard",
"value": "Discard"
},
{
"name": "In Article Writer",
"value": "In Article Writer"
}
],
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Status",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Description",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Description",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Keyword",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Primary Keyword",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Primary Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Category",
"type": "string",
"display": true,
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"text": "=Analyze ALL keywords as a complete dataset and identify natural semantic clusters. Let the number and size of clusters emerge from the relationships in the data. Each keyword should be assigned to its most relevant cluster based on semantic meaning and user intent.\n\nGuidelines for clustering:\n- Identify genuine thematic relationships across keywords\n- Group keywords based on semantic similarity and shared user intents\n- Consider how different keyword types complement each other in revealing topic patterns\n- Let cluster count and size be determined by the natural groupings in the data\n- Each keyword should belong to exactly one cluster\n\nReturn a single-line JSON object with this structure (no line breaks):\n{\n \"total_keywords_processed\": number,\n \"number_of_clusters\": number,\n \"clusters\": [\n {\n \"cluster_name\": \"descriptive name\",\n \"core_topic\": \"main theme or focus\",\n \"intent_pattern\": \"primary user intent pattern\",\n \"keywords\": [\"array of all keywords in this cluster\"],\n \"reasoning\": \"explanation of why these keywords form a coherent cluster\",\n \"primary_keyword\": \"the primary keyword\"\n }\n ]\n}\n\nIMPORTANT: Do not include extra spaces, backticks ```, or any comments about what you did. Do not include \\n in any part of the output.\n\nInput data:\n{{ $json.keyword_dataset }}\n\n",
"options": {
"systemMessage": "=You are an AI expert in semantic analysis and content clustering. Your task is to analyze a collection of keywords holistically and identify natural semantic clusters based on:\n- Thematic relationships\n- User intent patterns\n- Topic hierarchies\n- Search behavior signals across different keyword types (direct searches, questions, suggestions)\n\nConsider both keywords with metrics (search_intent) and those without (people also ask, subtopics, autocomplete) as they represent different aspects of user intent."
},
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{
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},
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{
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"assignments": [
{
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{
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"type": "n8n-nodes-base.stickyNote",
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"parameters": {
"color": 3,
"width": 540,
"height": 820,
"content": "# 设置指南"
},
"typeVersion": 1
},
{
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{
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},
{
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"value": "tblD8sMi6W4EikkN4"
},
{
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},
{
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},
{
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},
{
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},
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{
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"name": "Airtable 获取所有关键词",
"type": "n8n-nodes-base.airtable",
"position": [
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864
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"parameters": {
"base": {
"__rl": true,
"mode": "id",
"value": "={{ $json.airtable_base_id }}"
},
"table": {
"__rl": true,
"mode": "id",
"value": "={{ $json.master_all_kw_variations_table_id }}"
},
"options": {},
"operation": "search"
},
"credentials": {
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"typeVersion": 2.1
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{
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"name": "聚类创意表",
"type": "n8n-nodes-base.airtable",
"position": [
2240,
1504
],
"parameters": {
"base": {
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"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.airtable_base_id }}"
},
"table": {
"__rl": true,
"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.content_ideas_from_clusters_table_id }}"
},
"columns": {
"value": {
"Type": "={{ $json.type }}",
"Title": "={{ $json.title }}",
"Status": "Not Started",
"Keyword": "={{ $json.keyword }}",
"Reasoning": "={{ $json.reasoning }}",
"Description": "={{ $json.description }}",
"Cluster Name": "={{ $json.cluster_name }}",
"Primary Keyword": "={{ $('Set WF Fields').first().json.primary_keyword }}"
},
"schema": [
{
"id": "Cluster Name",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Cluster Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Title",
"type": "string",
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"removed": false,
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"required": false,
"displayName": "Title",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
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"type": "string",
"display": true,
"removed": false,
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"required": false,
"displayName": "Description",
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"canBeUsedToMatch": true
},
{
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"removed": false,
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"required": false,
"displayName": "Type",
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},
{
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"removed": false,
"readOnly": false,
"required": false,
"displayName": "Reasoning",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Status",
"type": "options",
"display": true,
"options": [
{
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},
{
"name": "Send to Article Writer",
"value": "Send to Article Writer"
},
{
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"value": "Complete"
},
{
"name": "Delete",
"value": "Delete"
}
],
"removed": false,
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"required": false,
"displayName": "Status",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
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"type": "string",
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"removed": false,
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"required": false,
"displayName": "Primary Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
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"displayName": "Keyword",
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}
],
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}
},
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},
{
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"name": "聚类表",
"type": "n8n-nodes-base.airtable",
"position": [
1872,
1168
],
"parameters": {
"base": {
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"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.airtable_base_id }}"
},
"table": {
"__rl": true,
"mode": "id",
"value": "={{ $('Set Airtable Fields').item.json.clusters_table_id }}"
},
"columns": {
"value": {
"Intent": "={{ $('Set Fields for Airtable').item.json.intent_pattern }}",
"Keywords": "={{ $json.keywords }}",
"Reasoning": "={{ $('Set Fields for Airtable').item.json.reasoning }}",
"Core Topic": "={{ $('Set Fields for Airtable').item.json.core_topic }}",
"Cluster Name": "={{ $('Set Fields for Airtable').item.json.cluster_name }}",
"Primary Keyword": "={{ $('Set Fields for Airtable').item.json.primary_keyword }}"
},
"schema": [
{
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"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "Cluster Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
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"removed": false,
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"required": false,
"displayName": "Core Topic",
"defaultMatch": false,
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},
{
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"removed": false,
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"required": false,
"displayName": "Intent",
"defaultMatch": false,
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},
{
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{
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},
{
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"required": false,
"displayName": "Primary Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
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},
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}
},
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},
{
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"name": "OpenAI 聊天模型1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
192,
544
],
"parameters": {
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"mode": "list",
"value": "chatgpt-4o-latest",
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},
"options": {}
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{
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816
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{
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"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
480,
1392
],
"parameters": {
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"mode": "list",
"value": "chatgpt-4o-latest",
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},
"typeVersion": 1.2
}
],
"active": false,
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},
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"connections": {
"Split Out": {
"main": [
[
{
"node": "Set Fields for Airtable",
"type": "main",
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}
]
]
},
"Split Out1": {
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{
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},
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{
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},
"Set WF Fields": {
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{
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]
},
"Category Table": {
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},
"OpenAI Chat Model": {
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},
"OpenAI Chat Model3": {
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},
"Set Airtable Fields": {
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[
{
"node": "Airtable Get All KWs",
"type": "main",
"index": 0
}
]
]
},
"Set Field for Agent": {
"main": [
[
{
"node": "AI Agent Analyze and Cluster KWs",
"type": "main",
"index": 0
}
]
]
},
"Airtable Get All KWs": {
"main": [
[
{
"node": "Set WF Fields",
"type": "main",
"index": 0
}
]
]
},
"Set Fields for Airtable": {
"main": [
[
{
"node": "Split Out1",
"type": "main",
"index": 0
}
]
]
},
"Agent Create Content Opps": {
"main": [
[
{
"node": "Edit Fields3",
"type": "main",
"index": 0
}
]
]
},
"Set Category Table Fields": {
"main": [
[
{
"node": "Category Table",
"type": "main",
"index": 0
}
]
]
},
"Aggregate Keywords for Agent": {
"main": [
[
{
"node": "Set Field for Agent",
"type": "main",
"index": 0
}
]
]
},
"Set Content Ideas Table Field": {
"main": [
[
{
"node": "Categories Content Ideas Table",
"type": "main",
"index": 0
}
]
]
},
"Categories Content Ideas Table": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"AI Agent Analyze and Cluster KWs": {
"main": [
[
{
"node": "Edit Fields1",
"type": "main",
"index": 0
},
{
"node": "Edit Fields2",
"type": "main",
"index": 0
}
]
]
},
"Set Content from Category Fields": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Set Airtable Fields",
"type": "main",
"index": 0
}
]
]
},
"Content Ideas from Category AI Agent": {
"main": [
[
{
"node": "Set Content Ideas Table Field",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
高级
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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