Natural fractures in tight sandstone reservoirs play an important role in hydrocarbon migration and accumulation. Fracture identification remains challenging due to the scarcity of labeled data and the complex logging responses of fractures. To address these problems, we propose a novel hybrid deep learning framework (CNN-Attention-BiLSTM) for labeled data balancing. First, labeled fracture classification based on full waveform sonic logs (FWS) characteristics is employed to screen unlabeled data, replacing sampling algorithms for data balancing. This approach provides conventional logging with more fracture labels that align with authentic geological information, thereby enhancing the reliability of fracture labels. Subsequently, one-dimensional convolution is applied to construct multi-dimensional fracture logging response patterns that characterize fracture development. A Channel Self-Attention (CSA) mechanism is introduced to assign optimal weights to response patterns across different dimensions, achieving an optimized pattern combination and thereby offering clearer response pattern guidance for subsequent identification models. A double-layer BiLSTM (DL-BiLSTM) is then utilized to mitigate the impact of sedimentary cycles on logging identification, while capturing both short- and long-term dependencies of fracture responses across different network layers. Ultimately, intelligent fracture identification is realized. The identification method is applied to the H1 member of the Lower Shihezi Formation in the Hangjinqi area, China. The test set accuracy is higher than 90%, and blind wells verification demonstrates an improvement of over 8% in accuracy compared to conventional methods. The identification results reveal that fractures are the most developed in H1-2 interval, followed by H1-1 and H1-3 intervals, while H1-4 interval is the least developed. The fracture distribution pattern is evidently controlled by both sedimentary rhythms and reservoir properties, resulting in complex storage and flow capabilities. The findings can provide guidance for the migration, accumulation and efficient development of tight sandstone gas.