Transmission lines are critical elements for the stable operation of power systems. When a fault occurs on a transmission line, the ability to quickly and accurately identify the cause of the fault is of great significance for the safe and stable operation of the power system. To address the low accuracy problem present in existing transmission line fault cause identification schemes, this paper proposes a method for identifying transmission line fault causes based on multi-source data fusion. First, external interference factors and waveform characteristics corresponding to different types of transmission line faults are analyzed to provide theoretical support for multi-source data input. Next, the Gramian Angular Field and feature encoding techniques are employed to preprocess fault information, constructing feature representations for different fault types from the perspectives of time-series waveforms, two-dimensional images, and discrete features. Furthermore, a method is designed that fuses adaptive boundary parameters with Long Short-Term Memory neural networks, Convolutional Neural Networks , and Artificial Neural Networks to classify and identify the causes of transmission line faults. Finally, the effectiveness and superiority of the proposed method are verified through comparative tests on real-world data, providing a reliable solution for accomplishing the high-precision task of transmission line fault cause identification.