Current fault identification methods based on deep learning generally face challenges such as high data dependency, high computational cost and time consumption, and limited model generalization ability. To address these issues, this study proposes a lightweight and high-precision fault identification model that integrates the MobileNetV3, selective kernel network (SKNet), and stacked long short-term memory network (Stacked LSTM). First, the input data is preprocessed, and the processed data is converted into a format suitable for convolutional layer input. In the feature extraction stage, an improved MobileNetV3 backbone network is employed for deep feature mining. On the basis of retaining the efficiency of depthwise separable convolution, an inverted residual module strategically embeds a dual attention mechanism combining squeeze-and-excitation (SE) and selective kernel (SK), effectively enhancing channel information interaction and multi-scale feature adaptive selection, thus significantly improving feature representation and reducing computational complexity. Subsequently, the stacked LSTM captures long-term temporal dependencies in the vibration signals. Finally, feature compression and classification decision-making are achieved through the fully connected layer, thus constructing an end-to-end recognition system. Experimental results show that the recognition accuracy of the proposed model reaches 99.47%, demonstrating significant advantages in recognition accuracy and model generalization ability compared with traditional gearbox fault identification techniques.