Research on Library Book Information Resource Management Based on Artificial Intelligence and Sensors

Abstract

In order to explore the research on library book information resource management, the author proposes a method based on artificial intelligence and sensors. Using an improved SVM algorithm, in order to realize the personalized data mining of the library, the support vector machine algorithm has supervised, scalable, and nonlinear high-efficiency characteristics in the use process, able to achieve nonlinear multicore data clustering effect, thereby improving the learning ability of data mining. The experimental results show the following: BP neural network was used to adaptively train the processed data samples, users give certain positive feedback during use, the sensor system is based on the result of feedback, continuous self-learning was carried out, sample data were updated and optimized, and a closed virtuous circle has been realized. In the classification experiment of Sogou Chinese text corpus data set, the classification effect of several classification models in the “sports” category is significantly higher than that of other categories; it shows that the text classification characteristics of the “sports” category are more significant. Among them, KNN ( $K$ -Nearest Neighbor) has a classification accuracy of 99.7% in the “sports” category; this shows that some classification algorithms, in a specific category, can achieve its best classification performance. Prove the method based on artificial intelligence; it can better realize library book information resource management.