Doctor recommendation method based on demand keyword analysis and doctor multi-dimensional influence evaluation

Abstract

This invention provides a doctor recommendation method based on demand keyword parsing and multi-dimensional doctor influence assessment. It extracts semantic information such as user symptom entities and modifiers through sliding window segmentation and deep learning models to generate a confidence-based intent representation. It adaptively detects user intent drift using cosine distance variation and outputs the intent evolution strength. It integrates data such as doctor clinical behavior flow and patient evaluations, and uses time-weighted convolution to measure doctor dynamic influence. It dynamically gates and adjusts the doctor influence weights based on intent drift strength, achieving an organic combination of behavior and historical reputation. Combining real-time doctor influence, expertise vectors, and resource supply, it calculates a comprehensive matching score, and then outputs a personalized list of top ten doctor recommendations, generating corresponding demand-focused explanation tags. This invention improves the sensitivity of the recommendation system to changes in user needs and enhances the timeliness and fairness of doctor ranking.

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