Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture. Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently. In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform, the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed. By combining context data acquisition, data analysis and matching, and personalized knowledge recommendation, a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data. Using the cloud platform for agricultural knowledge and agricultural intelligent service, this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers, including planting technology, disease control, expert advice, etc. Then the knowledge needs of different users can be met and user satisfaction can be improved.