Focusing on the prediction of customers' new car model purchase behavior, this paper carries out a comparative analysis on the performance of four classification algorithms: Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GaussianNB), and Support Vector Machine (SVM). Based on data related to car consumption, following data preprocessing, hyperparameter tuning is performed for each model. For Logistic Regression, the regularization parameters and penalty terms are optimized. For the KNN algorithm, the number of neighbors is adjusted, and an appropriate distance measurement method is selected. For Gaussian Naive Bayes, the adaptability of the feature independence assumption is verified, and corresponding fine-tuning is carried out. Optimization is performed on key parameters of SVM, including the kernel function and regularization coefficient. After training and testing evaluation, the SVM demonstrates outstanding comprehensive performance after tuning, with an AUC reaching 0.965 and a prediction accuracy of 93%. Both KNN and Gaussian Naive Bayes achieve an AUC of 0.963 and an accuracy of 93%, while Logistic Regression has an AUC of 0.955 and an accuracy of 91%. The research shows that SVM has significant advantages in fitting nonlinear data, is suitable for the car consumption prediction scenario, and provides powerful data support and decision basis for enterprises to develop targeted marketing strategies.