Main Article Content
To familiarize users with their interests and hobbies through online data collection, and improve their experience when browsing art works, research based on K-means algorithm has received widespread attention. However, with the explosive growth of various types of art works, it is difficult to estimate the K value of the K-means algorithm when processing these data. To solve this problem, this research predicts the user behavior of Wink dataset based on K-means algorithm, introduces regularization specified process and emotional precision, and generates fusion algorithm. The study first proposes the concept of similar users and calculates the Pearson correlation coefficient between them to determine their similarity; Then several regularization terms are added to the user group, and the prediction results are obtained by changing the parameters; Further screening of art works clustering categories is to address the issue of slow user startup. Finally, the algorithm studied will be applied to the Wink dataset and the prediction accuracy of the particle swarm optimization algorithm will be tested and compared with the fusion algorithm. A total of 400 experiments are conducted, and the fusion algorithm achieve a prediction accuracy of 392 times, with an accuracy rate of 98.0%; The accuracy of particle swarm optimization algorithm is close to that of fusion algorithm, at 88.2%. The experimental results show that the algorithm model proposed in the study can effectively map the relationship between user interest features, emotional factors, and personalized art recommendation, thereby providing users with a good viewing experience.