The rapid development of automotive intelligent technology has led to increased complexity of vehicle functions and a surge in the number of document developments, posing new challenges for the analysis of traditional vehicle electrical faults. In order to enhance the capability of electronic and electrical fault analysis and diagnosis, an intelligent diagnostic technology is utilized to construct an electrical data analysis model, which aids in the intelligent analysis and localization of faults in the automotive electrical system. The model construction is divided into two steps: firstly, the collection and preprocessing of electrical test data, including message parsing, data cleaning, and feature extraction; secondly, the use of deep learning algorithms to build a fault analysis model, achieving intelligent fault analysis and localization through model training. The proposed automotive electrical data analysis model based on intelligent diagnostic technology is capable of efficiently parsing complex electrical test data, realizing intelligent fault analysis and localization, and providing strong support for the troubleshooting of electronic and electrical faults.