Price prediction model of cultural products based on improved XGBoost

As computer technology rapidly advance, machine learning algorithms are widely used in the fields of data analysis and price prediction. Aiming at the problem of inaccurate price prediction of cultural products due to the different knowledge levels of consumers, the study proposes an improved extreme gradient boosting algorithm integrated with the particle swarm algorithm, and constructs a ceramic art price prediction model based on the improved extreme gradient boosting. To assess the effectiveness of the improved extreme gradient boosting algorithm, its performance is compared and analyzed with other algorithms. It was found that the accuracy and recall of the algorithm were 98.76% and 98.47%, respectively, which were better than the comparison algorithms. Subsequently, the performance of the price prediction model was analyzed in comparison with other models, and it was found that the prediction fit coefficient of the model was 0.973, which was better than the comparison model. The above outcomes denote that the algorithm and model proposed in the study are effective and help to improve the price prediction of ceramic artworks, which can provide a theoretical basis for the research field related to consumer price prediction of cultural products.