Aircraft cable networks play a crucial role in electrical, signal, and data transmission functions. During supersonic flight, aircraft cable networks face challenges such as high temperatures, vibrations, current overload, and low pressure, which affect the safety and reliability of the aircraft electrical system. This study designs a multi-sensor monitoring system for cable networks and a cable network health monitoring algorithm based on multi-sensor fusion. The monitoring system achieves functions including data collection, storage, and wireless transmission of voltage, current, temperature, acceleration, and pressure. In the preprocessing stage, the algorithm comprehensively considers the effects of steady-state and transient values such as high temperatures, vibrations, current overload, and low pressure on the health status of cable networks through normalization. For the health status classification part, a multi-layer classification network is designed to classify the cable network states. In both practical experimental datasets and simulated datasets, the multi-layer classification network in this study achieves an average increase in accuracy of 6.4% and a decrease in false alarm rate of 77.2% compared to the SVM classification network. Compared to single-channel monitoring algorithms, the multi-sensor monitoring algorithm in this study significantly improves accuracy. Experimental results validate the effectiveness of the algorithm in cable network health status classification tasks. The results indicate that the multi-sensor monitoring system for cable networks can effectively monitor and identify the health status of aircraft cable networks, providing strong assurance for the operation of aircraft electrical systems.