Cables occupy an irreplaceable position in the distribution network and are power equipment that must be paid attention to. Sudden power outages caused by cable failures will pose a serious threat to the safety of users’ lives and property, and even cause adverse social impact. Therefore, their intelligent operation, maintenance and repair problems also need to be solved urgently. Due to the significant room for improvement in key issues such as technical limitations and diagnostic standards for power cable fault warning, this paper proposes a distribution network cable fault warning mechanism based on a combination of data and models to address issues such as insufficient online monitoring and analysis of existing distribution network cables, fuzzy health status evaluation, and inadequate insulation fault warning mechanisms. By gaining a deeper understanding of cable insulation mechanisms, studying their degradation characteristics, analyzing and extracting characteristic values, constructing a composite fusion health evaluation mechanism, and forming a data-driven model. Then, the degradation index is modeled through a random process, and an objective function is constructed to achieve the “linkage” between data feature extraction and the stochastic modeling of the time-varying evolution process of the extracted features. This overcomes the problem of unclear physical meaning of the index, which leads to difficulty in determining its corresponding failure threshold, and provides a more accurate method and theory for online monitoring of electrical equipment faults.