Design and Implementation of a New Power System Technology Achievement Recommendation System for Sample Analysis

With the rapid development and technological innovation of the power system, the management and optimization of new power systems such as microgrids and distributed energy systems have become a research hotspot. This article introduces the design and implementation of the CRKG-STS model, which is a sample analysis and recommendation system for new power system technology achievements, this system combines matrix recovery technology and collaborative filtering algorithms to achieve real-time analysis of user needs and personalized technical achievement recommendations through dialogue processing, recommendation execution, and knowledge graph module collaboration. By utilizing Relationship Graph Convolutional Networks (RGCN) to enhance entity encoding and employing Transformer architecture for context sensitive text suggestion generation, the accuracy and efficiency of recommendations have been effectively improved. The experimental results show that CRKG-STS outperforms traditional methods in multiple performance indicators, significantly improving user experience and the matching effect of technological achievements.