关键词: 新能源汽车；二手车；事故鉴定；智能鉴定系统；多数据融合；CNN-LSTM 模型

Abstract: With the rapid growth in the ownership of new energy vehicles, the market scale of new energy used vehicles has continued to expand. However, the difficulty in identifying accidentdamaged vehicles has become a core bottleneck restricting the healthy development of the market. Traditional identification methods rely on manual experience, suffering from problems such as low efficiency, strong subjectivity, and inconsistent standards, which make it difficult to meet the needs of market standardization. This paper researches and designs an intelligent accident identification system for new energy used vehicles. By integrating body structure data, battery system data, electronic control system data, and historical maintenance data, the system adopts an improved deep learning algorithm based on convolutional neural network-long short-term memory (CNN-LSTM) to achieve accurate identification of accidents in new energy used vehicles. To verify the system performance, multi-dimensional actual data of 1 000 new energy used vehicles from mainstream brands were collected for model training and testing. The results show that the overall recognition accuracy of the system for accident-damaged vehicles reaches 96.8%, which is 8.2% higher than that of the unimproved CNN-LSTM model; the precision, recall, and F1-score reach 96.2%, 97.1%, and 96.5% respectively. This system provides an efficient and reliable technical solution for accident identification of new energy used vehicles and is of great significance for promoting the standardized development of the market.

Key words: new energy vehicles; used vehicles; accident identification; intelligent identification system; multi-data fusion; CNN-LSTM model