Research and Practice of User Learning Style Model Recognition Based on Big Data

The new education concept in the “Internet plus” era requires educators to focus on the personalized learning and development of users. Due to the massive learning resources in the network, it is difficult for users to make scientific choices according to their own learning characteristics and habits, resulting in low learning efficiency. How to use education big data analysis technology to provide accurate services for users, this paper uses big data analysis method to build user learning style recognition model, according to data preprocessing, big data key parameters fitting optimization, KNN algorithm to identify user learning style, and provide technical means for accurately pushing personalized learning resources and paths. Finally, according to the dynamic learning behavior data of users, based on the theory of Felder-Silverman learning style model, through the experimental comparison of two teaching classes, it shows that the research results can meet the learning needs, provide effective guidance for users, help to improve learning efficiency and promote personalized development.