Abstract: In the intelligent Connected vehicle (ICV), improving the effectiveness of driving data is the cornerstone of improving vehicle safety. Only accurate and reliable driving data can provide a reliable basis and support for vehicle safety. Compared with traditional anomaly analysis, ICV data validity analysis faces a diversity of data anomalies (sensor anomalies, driving behavior, malicious tampering, etc.). How to combine the vehicle's own data characteristics, driving style, and traffic flow characteristics to provide an effective data anomaly detection method has become a new problem in intelligent networked vehicles. For the ICV system, a TE-PSO-SVM data validity detection algorithm based on particle swarm optimization is designed by combining driving style and traffic flow theory to realize effective detection of driving data. Firstly, the driving style recognition coefficient Rad is defined and the driving style quantitative model is designed. Secondly, a traffic flow model is established, which combines driving style and traffic flow theory with vehicle state data to predict vehicle speed through the LSTM network. Finally, the TE-PSO-SVM algorithm was used to detect the validity of the data. Due to the diversity of ICV data, the detection accuracy of a single model is limited in scenarios where multiple types of anomalies coexist. To make use of the advantages of multiple models, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on real data set highD show that the F1 metric value of the TE-PSO-SVM algorithm model is 8.1 percentage points higher than that of the traditional SVM model under different noise environments. Compared with the algorithm with the highest detection rate in the model pool, the F1 metric value of the RLBMS algorithm model in different noise environments is increased by about 1.7 percentage points on average, which further improves the accuracy of data validity detection.