Construction and Optimization of Learning Resources Push Service Model in Smart Education Environment

With the integration of cloud computing, big data, artificial intelligence and other technologies, the smart education model came into being, providing learners with a personalized, intelligent and ubiquitous learning environment. Firstly, this paper analyzes the importance of learner feature recognition, and puts forward a comprehensive recognition system combining self-evaluation and Felder-Silverman scale. Then, a set of learning resource matching method is designed, which quantifies the characteristics of learners and uses cosine similarity algorithm to realize the accurate matching between learners' characteristics and learning resources. In addition, this paper also discusses the strategy of improving push accuracy and optimizing user experience, including the application of machine learning algorithm, real-time data fusion mechanism, friendly user interface design, and enhancing interaction. Finally, it is emphasized that the security and privacy protection of user data must be put in the first place in the optimization of learning resource push service model in the smart education environment, and measures such as encryption algorithm, access control, data minimization and anonymous processing are put forward to ensure data security. Through these comprehensive measures, this paper aims to provide theoretical support and practical guidance for the in-depth development of wisdom education, so as to improve learners' learning efficiency and satisfaction.