Objective. To explore the construction of immersive learning system based on virtual reality (VR) and test its learning effect. Methods. 20 qualified subjects were divided into two groups, each tending to agree, each with 5 boys and 5 girls. Experimental group 1 is in the real operating environment, and experimental group 2 is in the VR virtual disassembly experimental environment. The task process errors, knowledge questionnaire scores and user subjective satisfaction were analyzed statistically. Results. The significance probability P of knowledge questionnaire in the Levene test was 0.777, greater than 0.05, and the variance homogeneous, so the final P of independent sample t -test was subject to “assumed variance equal”; the significance probability was 0.613, greater than 0.05; the questionnaire scores of two groups showed no significant difference, so VR virtual environment can achieve the learning effect of the real environment. The number of errors in a VR virtual situation is significantly lower than the number of errors in the real environment, the VR virtual environment can achieve the learning effect of the real environment, and the VR virtual environment can achieve more interaction, with good interaction. Conclusion. The immersive learning system based on VR detection technology can realize the cognition of three-dimensional model structure and has a certain learning effect.
Information overload will undoubtedly increase the time for users to search for information on the Internet. Data mining technology based on big data is constantly improving this situation. It extracts hidden, previously unknown information from massive data information to find out the internal connection between users, between the user and the item, and between items, so as to recommend information according to the user’s preferences and needs, and even provide the user with a customized page. So how does it implement personalized recommendations? This article takes online digital movie recommendation as an example, and explains the principle and process of collaborative filtering recommendation algorithm to achieve personalized recommendation through the combination of theoretical analysis and experiment.
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