The designed service life of cross-linked polyethylene (XLPE) cables is typically 30~40 years. The service life of cables in some early urbanized areas is gradually approaching this limit, leading to frequent faults that seriously impair the reliability of power systems. Cable joints are the weak link in cable lines, and their faults account for approximately 31% of all cable faults. In response to the above characteristics, this paper proposes a multi-strategy fusion cable joint fault diagnosis model (AKF-MTF-GADF-ViT-BiLSTM) based on the improved cuckoo-catfish optimization algorithm (ICCOA). First, the model uses adaptive Kalman filtering (AKF) to perform denoising and restoration processing on the original data, which effectively suppresses the nonlinear interference caused by environmental disturbances at the acquisition site and sensor jitter. Then, Markov transition field (MTF) and gramian angular difference field (GADF) are utilized to capture the dynamic and static fusion features of the data. A dual-path Vision Transformer (ViT) is adopted to extract the multi-dimensional correlation information of the features. Finally, the processed features are input into a bidirectional long shortterm memory network (BiLSTM) to learn the spatial features and temporal dependencies, yielding the final fault diagnosis results. In this article, ICCOA is introduced to tune parameters such as the number of feedforward network hidden units, the number of attention heads, and the decay factor of the ViT-BiLSTM model. This optimization addresses the problems of slow convergence and overfitting of the original model. The proposed model is verified for multi-fault diagnosis using measured data from an experimental platform and simulation data from SolidWorks Simulation. The overall diagnostic accuracy reaches 95.1%, which is 24.8% higher than that of the ablation model without filtering, 31.9% and 17.0% higher than those of the two ablation models with single-mode feature extraction respectively, and up to 10.9% higher than the three mainstream industry models of 1D-CNN-BiLSTM, CNN-LSTM, and VGAF-CLT. These results demonstrate that the proposed method effectively overcomes the limited generalizability of single models and improves the accurate identification of multiple cable joint fault types.