Aiming at the problem of the high failure rate of electric vehicle charging piles, a fault prediction method of electric vehicle charging piles based on cooperative game strategy and dung beetle optimization algorithm-bidirectional long-term and short-term memory network-attention mechanism (DBO-BiLSTM-Attention) is proposed. Firstly, abnormal values are processed through parameter statistical distribution, missing values are processed through mean imputation, and the processed data is normalized. Secondly, from different perspectives, subjective evaluation methods such as analytic hierarchy process, objective evaluation method CRITIC weighting method, and machine learning algorithm random forest are selected to calculate feature weights in sequence. Cooperative game strategy is used to combine the above feature weights to obtain new feature weights, and the parameter feature matrix is enlarged. Then, the beetle optimization algorithm and attention mechanism were introduced separately to build the DBO BiLSTM Attention model. Under simulation experiments, the accuracy and F1 coefficient of the training and testing sets of this model were 0.89, 0.89, 0.90, and 0.90, respectively. Finally, relevant comparative experiments were conducted, and the results showed that compared to the model without feature amplification, the accuracy and F1 coefficient of the test set were improved by 5% and 6%, respectively; Compared with the model that does not adopt cooperative game strategy, the accuracy and F1 coefficient of the test set have been improved by 2% and 3% respectively, verifying the effectiveness and rationality of the proposed model.