This study is devoted to improving the accuracy and efficiency of automobile noise fault diagnosis by using deep learning technology. By constructing a convolutional neural network (CNN) model, we deeply analyze and learn the automobile noise data, aiming at realizing the automatic identification of different types of faults. The experimental results show that the deep learning model shows significant improvement in several performance indicators compared with the traditional methods. Different parameter configurations are used to train the deep learning model. The model structure includes a convolution layer, a maximum pooling layer, and a full connection layer. By extracting the hierarchical features of the data, the model can better adapt to the complex characteristics of noise data. Compared with traditional methods, the CNN model has achieved obvious advantages in accuracy, recall, and F1 score. The research results not only verify the effectiveness of deep learning in automobile noise fault diagnosis but also show the robustness and adaptability of the model under more complex parameter configurations. The conclusion of this study provides empirical support for the application of deep learning technology in the field of automobile noise fault diagnosis.
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