Research on Used Car Valuation Model Based on SVM Fusion Learning

As the global automobile trading market continues to expand, the valuation problem in used car trading becomes increasingly important. The current methods for evaluating used car prices involve complex steps and subjective evaluation results. The SVM-based used car valuation model addresses this issue by employing advanced methods, simplifying the steps, and reducing subjectivity. In this paper, we first utilize web crawler technology to collect and process data. We then analyze the correlation between data indicators. Next, we employ a support vector machine (SVM) for price prediction, utilizing feature engineering and principal component analysis for feature selection. We combine the selected feature indicators with greater information importance with the SVM model to enhance the prediction accuracy, thus optimizing the model’s effectiveness for used car price evaluation. To validate the success of our used car valuation approach, we use SPSS to analyze the correlation of data indicators. The experiment confirms that the SVM-based fusion learning used car valuation model exhibits high accuracy and serves as an important guide for the development of the used car market.