Research on the Application of PSO-BP Neural Network Model in the Pricing Market of Second-hand Sailboats

This study addresses the challenge of pricing used sailboats in trading by employing the PSO-BP neural network model for price prediction in the secondary sailboat market. Initially, data processing and analysis are conducted to identify key factors influencing sailboat prices, followed by feature selection. Subsequently, the PSO algorithm is utilized to optimize the initial weights and biases of the BP neural network, enhancing prediction accuracy. Finally, experimental validation is performed, comparing the PSO-BP neural network model with conventional BP neural networks and support vector regression models. The findings demonstrate that the PSO-BP neural network model exhibits superior prediction accuracy and strong generalization capabilities, making it a viable approach for forecasting prices in the secondary sailboat market.