With the rapid proliferation of new energy vehicles, the safety of power batteries has attracted increasing attention. As a crucial approach to ensuring system stability, fault detection has become a research focus. However, strong temporal dependencies in battery operation data and the scarcity of fault samples hinder the accuracy and robustness of existing methods. To address these challenges, this paper proposes a deep learning-based fault detection model that integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory (CNN-LSTM) network. The GAN is employed to augment minority-class fault samples, effectively mitigating the class imbalance in the dataset. Then, the CNN-LSTM module directly processes raw multivariate time-series data, combining the capability of CNN in extracting local spatial patterns with the LSTM strength in modeling temporal dependencies, enabling accurate identification of battery faults. Experiments conducted on real-world datasets collected from electric vehicles demonstrate that the proposed model achieves a Precision of 95.23%, Recall of 87.23%, and F1-Score of 91.12% for fault detection. Additionally, it yields an Average Precision (AP) of 97.45% and an Area Under the ROC Curve (AUC) of 99%, significantly outperforming conventional deep learning and machine learning baselines. This study provides a practical and high-performance solution for fault detection in power battery systems, with promising application potential.

Background: Implementing automatic classification of short texts in online healthcare platforms is crucial to increase the efficiency of their services and improve the user experience. A short text classification method combining the keyword expansion technique and a deep learning model is constructed to solve the problems of feature sparsity and semantic ambiguity in short text classification. Methods: First, we use web crawlers to obtain patient data from the online medical platform “Good Doctor”; then, we use TF-IWF to weight the keyword importance and Word2vec to calculate the keyword similarity to expand the short text features; and then we integrate the cue learning and deep learning models to construct a self-adaptive attention model to solve the problem of sparse features and unclear semantics in short text classification in the adaptive-attention-Prompt-BERT-RCNN model to realize effective classification of medical short texts. Results: Empirical studies show that the classification effect after keyword expansion is significantly higher than that before expansion, the accuracy of the model in classifying medical short texts after expansion is as high as 97.84%, and the model performs well in different categories of medical short texts. Conclusions: The short text expansion methods of TF-IWF and Word2vec make up for the shortcomings of not taking into account the keyword rarity and the contextual information of the subwords, and the model can achieve effective classification of medical short texts by combining them. The model further improves the classification accuracy of short text by integrating Prompt’s bootstrapping, self-adaptive attention’s keyword weight weighting, BERT’s deep semantic understanding, and RCNN’s region awareness and feature extraction; however, the model’s accuracy in individual topics still needs to be improved. The results show that the recommender system can effectively improve the efficiency of patient consultation and support the development of online healthcare.