AI Industry Hotspot Collector

Problem

Content teams often face mixed feeds from Baidu Hot Search, Weibo Hot Search, Douyin Hot List, and Zhihu Hot List: high noise, cross-platform duplication, and weak relevance to a specific vertical. Raw rankings still require manual keyword filtering, relevance judgment, and sorting, which can miss useful signals or mistake unrelated trends for opportunities.

How it works

AI Industry Hotspot Collector uses hotspot-aggregator as the underlying collection source and adds industry filtering plus relevance scoring before hotspots enter the workflow. The typical flow includes:
- Loading an industry configuration and extracting 3 to 20 monitored keywords; missing configuration triggers a direct prompt to load it first.
- Calling the collector to retrieve last 24 hours of hotspots from Baidu, Weibo, Douyin, and Zhihu.
- Matching titles and snippets with weighted rules: exact matches score 3, semantic associations score 2, and fuzzy matches score 1.
- Ranking by relevance and outputting a Top 10 industry hotspot list with heat scores and relevance scores.
- Falling back to web_fetch for Baidu Hot Search if the underlying call fails, or returning general industry hotspots marked as lower relevance when no strong matches exist.

Boundaries

It is well suited to daily hotspot checks for a fixed industry and keyword set, such as AI, cross-border e-commerce, SaaS, or incubator niches. The boundaries are explicit: an industry configuration is required, the number of monitored keywords is capped, output is limited to the last 24 hours, and the Top 10 needs at least three items with 7+ relevance. For full-web public opinion monitoring, multilingual reporting, or long-term trend modeling, a broader collection and evaluation pipeline is needed.