In the field of electrical power material management, it is paramount that users receive accurate recommendations regarding the electrical power materials they require. In recent years, increasing attention has been given to Knowledge Graph-based recommender systems for their capacity to effectively tackle cold start and data sparsity challenges. However, many of these systems still lack the capability to consider users’ historical behavioral patterns and model dynamic changes in user interests. To remedy this gap, the paper proposes an Electrical Material Recommendation model based on Temporal Preference and Knowledge aware collaborative attentive network, named EMR-TPK. EMR-TPK establishes associations between users and materials through knowledge graph embedding along with an attention mechanism. It utilizes a temporal dynamic attention network to characterize users’ temporal interests and historical behavioral patterns. By incorporating these patterns into the model, more accurate material recommendations can be generated. We conducted extensive experiments across three datasets: electric power materials, music, and books. Our results indicate that the EMR-TPK model exceeds advanced methods in recommendation accuracy, with AUC improvements of 0.36% for electric power materials, 0.95% for music, and 2.54% for books.