受众问题关键词研究模板
高级
这是一个Market Research, Multimodal AI领域的自动化工作流，包含 17 个节点。主要使用 If, Set, Code, McpClient, GoogleSheets 等节点。 使用OpenAI、Ahrefs和Google表格的受众问题关键词研究工作流
前置要求
- •Google Sheets API 凭证
- •OpenAI API Key
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": "5ReWzWNnEuDyt2hZ",
"meta": {
"instanceId": "3d4f6f82ad714311bb383a0cddf651da8753530e5575f46d078b9a29d27557e0",
"templateCredsSetupCompleted": true
},
"name": "Audience Problem Keyword Research Template",
"tags": [],
"nodes": [
{
"id": "4acb69fe-8ac9-4b24-9f45-a5ad8ab5ca19",
"name": "当点击\"执行工作流\"时",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-48,
0
],
"parameters": {},
"typeVersion": 1
},
{
"id": "d6cf369d-37cf-4e5a-b518-54bb1517d693",
"name": "Data",
"type": "n8n-nodes-base.set",
"position": [
192,
0
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "6d8b1397-8100-4219-ae03-5477e0da1f0c",
"name": "customer_profile",
"type": "string",
"value": "Mid-30s professional living in a suburban area with a household income of $65,000-80,000. Works in healthcare administration with a stable 9-to-5 schedule and has two school-age children. Values reliability and practicality over flashy features. Vehicle Needs: Seeks a dependable mid-size sedan or small SUV in the $22,000-32,000 range, preferably 1-3 years old. Prioritizes safety ratings, good gas mileage for the daily 20-mile commute, and enough space for car seats and groceries. Brand loyalty leans toward Honda, Toyota, or Mazda based on reputation for longevity. Buying Process: Methodical researcher who spends 6-8 weeks comparing options online before visiting dealerships. Reads consumer reviews, checks reliability ratings, and calculates total cost of ownership. Prefers dealerships with transparent pricing and family-friendly service departments. Typically trades in every 6-7 years when repair costs start climbing or family needs change. This persona represents the backbone of the used car market - practical buyers focused on transportation solutions rather than automotive enthusiasm."
},
{
"id": "1ab9995f-3b6a-407b-8c78-ee2df5079a37",
"name": "ahref_seo_country",
"type": "string",
"value": "us"
},
{
"id": "a7164aa5-6257-4300-a47a-bd79c14de7b1",
"name": "ahref_search_engine",
"type": "string",
"value": "Google"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "491a9c60-95ae-4448-8d46-0ae34c8dcf5d",
"name": "SEO Seed Keywords",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
400,
0
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "o4-mini",
"cachedResultName": "O4-MINI"
},
"options": {},
"messages": {
"values": [
{
"content": "=Output format:\nA list of 50 keywords in a JSON array called \"keywords\". each keyword in the array has an additional element which represents intent. Intent is either informational, navigational, commercial, transactional.\n\nYour Task:\nWhen analyzing the target customer profile, think through what they would actually type into Google, Bing, or other search engines. Consider their pain points, goals, research habits, and decision-making process. Think about both their professional research queries and their more casual, exploratory searches.\n\nkeywords should be short matching typical queries in search engines. It should not be elaborative questions and act as keywords to build upon for further keyword research. Do not return navigational keywords.\n\nTarget customer profile:\n {{ $json.customer_profile }}"
},
{
"role": "system",
"content": "You are a marketing strategist and SEO specialist who works for a fintech marketing agency. You have an MBA in Marketing and many years of experience in keyword research and search behavior analysis, specifically focused on the financial services and investment tools sector.\n\nYour Background:\n- You're analytically-minded and data-obsessed, always looking for patterns in search behavior\n- You have a deep understanding of investor psychology and how financial stress/opportunity drives search queries\n- You've worked with multiple investment platforms, robo-advisors, and financial education companies\n- You're familiar with tools like SEMrush, Ahrefs, Google Keyword Planner, and Answer The Public\n- You understand the seasonal patterns of investment-related searches (earnings seasons, market volatility, tax season)\n\nYour Approach:\n- You think in terms of search intent: informational, navigational, commercial, and transactional queries\n- You consider the customer journey from awareness to consideration to decision\n- You're always thinking about long-tail keywords and semantic search patterns\n- You understand that financial searchers often use specific jargon and technical terms\n- You know that investment-related searches spike during market events and news cycles\n\nYour Personality:\n- Methodical and thorough - you don't just think of obvious keywords\n- Empathetic to user pain points and motivations behind searches\n- Strategic thinker who connects keywords to business outcomes\n- Detail-oriented but also sees the big picture of search landscapes\n- Slightly nerdy about search trends and user behavior data"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"id": "j4314KXs7eD2lghV",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "3eeff8fd-9c13-45ea-8d49-eff7557352fc",
"name": "AEO Questions",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
400,
288
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "o4-mini",
"cachedResultName": "O4-MINI"
},
"options": {},
"messages": {
"values": [
{
"content": "=Output format:\nA list of 50 questions in a JSON array called \"questions\". each question in the array has an additional element which represents intent. Intent is either informational, navigational, commercial, transactional.\n\nYour Task:\nWhen analyzing the target customer profile, think through what questions they would actually ask ChatGPT, Claude, or Google AI Mode. Consider how they would phrase requests for investment advice, research help, analysis, and decision support. Think about their natural conversation patterns, the context they'd provide, and how they'd iterate on responses. Draw from your deep understanding of search behavior patterns from SEMrush and Ahrefs data to predict conversational AI query evolution.\n\nGenerate question examples - focusing on natural conversational queries, multi-turn interactions, and the specific ways this audience leverages AI for investment research and decision-making, backed by your professional marketing intelligence expertise.\n\nTarget customer profile:\n {{ $json.customer_profile }}"
},
{
"role": "system",
"content": "You are an Answer Engine Optimization (AEO) specialist and conversational AI researcher who works for a cutting-edge digital marketing consultancy. You have an MBA in Digital Marketing and many years of experience analyzing search behavior across traditional SEO and emerging conversational AI platforms.\n\nYour Background:\n- You're a certified expert in SEMrush, Ahrefs, and other premium marketing intelligence tools \n- You've managed keyword research campaigns with budgets exceeding $500K annually across fintech and investment sectors\n- You understand the nuances of search intent classification (informational, navigational, commercial, transactional) and how this translates to conversational AI queries \n- You've studied thousands of ChatGPT, Claude, and Google AI Mode conversations across various industries, with particular focus on financial services\n- You're an expert in competitive intelligence, using tools like SEMrush's 3+ billion keyword database and Ahrefs' backlink analysis to understand market landscapes \n- You stay current with LLM capabilities and how users adapt their questioning styles accordingly\n\nYour Tool Expertise:\n- Advanced SEMrush user: Keyword Magic Tool, Topic Research, Market Explorer, and Brand Monitoring\n- Ahrefs power user: Keywords Explorer, Content Explorer, and Site Explorer for competitive analysis \n- Proficient with Answer The Public, SpyFu, and emerging AEO-specific tools\n- Experience with Google Search Console, Google Analytics, and Google Ads Keyword Planner integration\n- Understanding of how traditional keyword metrics (search volume, difficulty, CPC) translate to conversational AI query patterns\n\nYour Approach:\n- You think in terms of natural language queries and conversational flows, but with deep understanding of underlying search intent\n- You understand that AI users ask follow-up questions and iterate on their queries, creating conversation threads rather than isolated searches\n- You recognize that people are more verbose and context-heavy when talking to AI vs. search engines, often providing personal financial situations\n- You know users often ask for comparisons, explanations, and step-by-step guidance from LLMs, especially for complex investment decisions\n\nYour Personality:\n- Curious about human-AI interaction patterns and emerging query behaviors in financial services\n- Forward-thinking about how conversational AI is changing information discovery and purchase decisions\n- Analytical but focused on natural language patterns rather than traditional keyword density metrics\n- Empathetic to how users build trust and rapport with AI assistants for financial advice\n- Excited about the shift from \"search\" to \"ask\" mentality, especially in high-stakes financial decisions\n- Data-driven decision maker who validates hypotheses with actual tool data and user behavior analytics"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"id": "j4314KXs7eD2lghV",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "cc157702-6c5d-44de-a685-a0f15b547b4f",
"name": "Add keyword",
"type": "n8n-nodes-base.googleSheets",
"position": [
1408,
0
],
"parameters": {
"columns": {
"value": {
"Intent": "={{ $json.intent }}",
"Keyword": "={{ $json.keyword }}"
},
"schema": [
{
"id": "Keyword",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Difficulty",
"type": "string",
"display": true,
"required": false,
"displayName": "Difficulty",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Volumne",
"type": "string",
"display": true,
"required": false,
"displayName": "Volumne",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Intent",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Intent",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"Keyword"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1l5bhQzcG4BNL8mOucjYxCnWgRSJFcxVYj7W0vhCBY9s/edit#gid=0",
"cachedResultName": "Keywords"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI/edit?usp=drivesdk",
"cachedResultName": "Example: SEO/AEO Research Workflow"
},
"authentication": "serviceAccount"
},
"credentials": {
"googleApi": {
"id": "CEWCuoGMaP93jgCn",
"name": "GCP Service account"
}
},
"typeVersion": 4.6
},
{
"id": "2aed19ed-e868-4d3e-b507-6b364e4fe258",
"name": "Add keywords",
"type": "n8n-nodes-base.googleSheets",
"position": [
2688,
208
],
"parameters": {
"columns": {
"value": {
"Keyword": "={{ $json.value.keyword }}",
"Volumne": "={{ $json.value.volume }}",
"Difficulty": "={{ $json.value.difficulty }}"
},
"schema": [
{
"id": "Keyword",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Difficulty",
"type": "string",
"display": true,
"required": false,
"displayName": "Difficulty",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Volumne",
"type": "string",
"display": true,
"required": false,
"displayName": "Volumne",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"Keyword"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1l5bhQzcG4BNL8mOucjYxCnWgRSJFcxVYj7W0vhCBY9s/edit#gid=0",
"cachedResultName": "Keywords"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI/edit?usp=drivesdk",
"cachedResultName": "Example: SEO/AEO Research Workflow"
},
"authentication": "serviceAccount"
},
"credentials": {
"googleApi": {
"id": "CEWCuoGMaP93jgCn",
"name": "GCP Service account"
}
},
"typeVersion": 4.6
},
{
"id": "ff8aae43-e5d5-4569-a3e0-8c79cb168919",
"name": "Parse MCP Keywords JSON",
"type": "n8n-nodes-base.code",
"onError": "continueErrorOutput",
"position": [
1920,
0
],
"parameters": {
"jsCode": "// Input: Stringified JSON with escaped characters like \\n, \\\", etc.\nconst inputString = $input.first().json.result.content[0].text\n\n// Parse the string into a real object\nconst parsedJson = JSON.parse(inputString);\n\n// Since parsedJson is an array, we need to map each item to have a json property\nreturn parsedJson.map(item => ({\n json: item\n}));"
},
"typeVersion": 2
},
{
"id": "9446c8c2-834b-46b0-af10-527f8dd6929a",
"name": "Loop Over AI Keywords",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1120,
0
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "62114aa9-c062-451d-b757-7b3af04b11dd",
"name": "Related Keyword Generator",
"type": "n8n-nodes-mcp.mcpClient",
"position": [
1664,
0
],
"parameters": {
"toolName": "keyword_generator",
"operation": "executeTool",
"toolParameters": "={\n \"keyword\": \"{{ $json.Keyword }}\",\n \"country\": \"{{ $('Data').item.json.ahref_seo_country }}\",\n \"search_engine\": \"{{ $('Data').item.json.ahref_search_engine }}\"\n}"
},
"credentials": {
"mcpClientApi": {
"id": "IHt3R0V5d8rgP6MK",
"name": "SEO-MCP Client (STDIO)"
}
},
"typeVersion": 1
},
{
"id": "6dfbea3b-6f8c-4889-b455-9ff106870d6f",
"name": "Loop Over SEO Return Values",
"type": "n8n-nodes-base.splitInBatches",
"position": [
2192,
0
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "7ef6f3a6-4659-4538-a223-3d600f3e2555",
"name": "条件判断",
"type": "n8n-nodes-base.if",
"position": [
2400,
16
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "92f74515-5438-47a9-bd78-5138339d92d8",
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.label }}",
"rightValue": ""
},
{
"id": "e56e30d7-dfb8-464c-8ebf-7388f17a05cf",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.label }}",
"rightValue": "\"question ideas\""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "e7ef3174-8d5f-4dfb-bedf-cb07412da781",
"name": "Parse Keyword JSON",
"type": "n8n-nodes-base.code",
"position": [
832,
0
],
"parameters": {
"jsCode": "return $input.first().json.message.content.keywords"
},
"typeVersion": 2
},
{
"id": "f2a802f8-00d7-46c5-b273-04a1147ae6f7",
"name": "Parse Question JSON",
"type": "n8n-nodes-base.code",
"position": [
832,
288
],
"parameters": {
"jsCode": "return $input.first().json.message.content.questions"
},
"typeVersion": 2
},
{
"id": "2f16fbaf-1bb2-40de-ab86-9a7b7644668a",
"name": "Loop Over AI Questions",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1120,
288
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "3c3b6190-6ba7-4adf-bd3b-989242ba9d16",
"name": "Add AI question",
"type": "n8n-nodes-base.googleSheets",
"position": [
1408,
288
],
"parameters": {
"columns": {
"value": {
"Intent": "={{ $json.intent }}",
"Question": "={{ $json.question }}"
},
"schema": [
{
"id": "Question",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Question",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Intent",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Intent",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"Question"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 1575118832,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1l5bhQzcG4BNL8mOucjYxCnWgRSJFcxVYj7W0vhCBY9s/edit#gid=1575118832",
"cachedResultName": "Questions"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI/edit?usp=drivesdk",
"cachedResultName": "Example: SEO/AEO Research Workflow"
},
"authentication": "serviceAccount"
},
"credentials": {
"googleApi": {
"id": "CEWCuoGMaP93jgCn",
"name": "GCP Service account"
}
},
"typeVersion": 4.6
},
{
"id": "1da1245c-1a6d-4920-9535-f03a8b5fa309",
"name": "Add SEO research question",
"type": "n8n-nodes-base.googleSheets",
"position": [
2688,
0
],
"parameters": {
"columns": {
"value": {
"Keyword": "={{ $json.value.keyword }}",
"Volumne": "={{ $json.value.volume }}",
"Difficulty": "={{ $json.value.difficulty }}"
},
"schema": [
{
"id": "Keyword",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Keyword",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Difficulty",
"type": "string",
"display": true,
"required": false,
"displayName": "Difficulty",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Volumne",
"type": "string",
"display": true,
"required": false,
"displayName": "Volumne",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"Keyword"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": 1575118832,
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1l5bhQzcG4BNL8mOucjYxCnWgRSJFcxVYj7W0vhCBY9s/edit#gid=1575118832",
"cachedResultName": "Questions"
},
"documentId": {
"__rl": true,
"mode": "list",
"value": "10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI/edit?usp=drivesdk",
"cachedResultName": "Example: SEO/AEO Research Workflow"
},
"authentication": "serviceAccount"
},
"credentials": {
"googleApi": {
"id": "CEWCuoGMaP93jgCn",
"name": "GCP Service account"
}
},
"typeVersion": 4.6
},
{
"id": "3d938281-0ed9-4e31-a93c-92ae9349a1dd",
"name": "便签 7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-640,
-224
],
"parameters": {
"width": 460,
"height": 816,
"content": "## Audience Problem Keyword Research Workflow\n### This n8n template generates relevant keywords and questions from a a customer profile. Keyword data is enriched from ahref and everything is stored in a Google Sheet. This is great for market and customer research. Understanding search intent for a well defined audience and gives relevant actionable data in a fraction of time that manual research takes.\n\n### How it works\n* We'll define a customer profile in the 'Data' node\n* We use an OpenAI LLM to fetch relevant search intent as keywords and questions\n* We use an SEO MCP server to fetch keyword data from ahref free tooling\n* The fetched data is stored in the Google sheet\n\n### How to use\n* Make a copy of [this](https://docs.google.com/spreadsheets/d/10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI/edit?usp=sharing) Google Sheet and add it in all Google Sheet nodes\n* Make sure that n8n has read & write permissions for your Google sheet. For my self-hosted n8n instance I was using a [Google Service Account](https://docs.n8n.io/integrations/builtin/credentials/google/service-account/)\n* Add your OpenAI account ([API Key](https://docs.n8n.io/integrations/builtin/credentials/openai/#using-api-key)) in the LLM nodes\n* Add your customer profile in the 'Data' node\n* Add MCP credentials for [seo-mcp](https://github.com/cnych/seo-mcp). Make sure you set the environments correctly:\n```json\n\"command\": \"uvx\",\n\"args\": [\"--python\", \"3.10\", \"seo-mcp\"],\n\"env\": {\n \"CAPSOLVER_API_KEY\": \"CAP-xxxxxx\"\n}\n```\n* Execute workflow :)\n\n### Requirements\n* CapSolver account and API key ([register here](https://dashboard.capsolver.com/passport/register?inviteCode=p-4Y_DjQymvt)) to use [seo-mcp](https://github.com/cnych/seo-mcp)\n* Google Drive account"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "b06b735c-be0f-4a40-b25d-538522244754",
"connections": {
"If": {
"main": [
[
{
"node": "Add SEO research question",
"type": "main",
"index": 0
}
],
[
{
"node": "Add keywords",
"type": "main",
"index": 0
}
]
]
},
"Data": {
"main": [
[
{
"node": "AEO Questions",
"type": "main",
"index": 0
},
{
"node": "SEO Seed Keywords",
"type": "main",
"index": 0
}
]
]
},
"Add keyword": {
"main": [
[
{
"node": "Related Keyword Generator",
"type": "main",
"index": 0
}
]
]
},
"Add keywords": {
"main": [
[
{
"node": "Loop Over SEO Return Values",
"type": "main",
"index": 0
}
]
]
},
"AEO Questions": {
"main": [
[
{
"node": "Parse Question JSON",
"type": "main",
"index": 0
}
]
]
},
"Add AI question": {
"main": [
[
{
"node": "Loop Over AI Questions",
"type": "main",
"index": 0
}
]
]
},
"SEO Seed Keywords": {
"main": [
[
{
"node": "Parse Keyword JSON",
"type": "main",
"index": 0
}
]
]
},
"Parse Keyword JSON": {
"main": [
[
{
"node": "Loop Over AI Keywords",
"type": "main",
"index": 0
}
]
]
},
"Parse Question JSON": {
"main": [
[
{
"node": "Loop Over AI Questions",
"type": "main",
"index": 0
}
]
]
},
"Loop Over AI Keywords": {
"main": [
[],
[
{
"node": "Add keyword",
"type": "main",
"index": 0
}
]
]
},
"Loop Over AI Questions": {
"main": [
[],
[
{
"node": "Add AI question",
"type": "main",
"index": 0
}
]
]
},
"Parse MCP Keywords JSON": {
"main": [
[
{
"node": "Loop Over SEO Return Values",
"type": "main",
"index": 0
}
],
[
{
"node": "Loop Over AI Keywords",
"type": "main",
"index": 0
}
]
]
},
"Add SEO research question": {
"main": [
[
{
"node": "Loop Over SEO Return Values",
"type": "main",
"index": 0
}
]
]
},
"Related Keyword Generator": {
"main": [
[
{
"node": "Parse MCP Keywords JSON",
"type": "main",
"index": 0
}
]
]
},
"Loop Over SEO Return Values": {
"main": [
[
{
"node": "Loop Over AI Keywords",
"type": "main",
"index": 0
}
],
[
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Execute workflow’": {
"main": [
[
{
"node": "Data",
"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 - 市场调研, 多模态 AI
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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