The non-contact voltage measurement method is not in direct contact with the metal conductor of the line and can adapt to the voltage monitoring in a variety of application scenarios. This paper designs a system which uses the improved non-contact voltage measurement technology to measure the line voltage and applies the measured voltage waveform to the line fault voltage diagnosis. Based on the topology analysis of the traditional non-contact voltage measurement technology and the improvement of the measurement circuit topology, the voltage on the line can be measured accurately without being affected by the coupling capacitance. Because of the limitation of the current single fault feature extraction method, in order to accurately identify and diagnose the line fault voltage by using the voltage waveform measured by the non-contact voltage measurement technology, in this paper, a fault voltage state identification system based on integrated learning is proposed, and a variety of feature extraction methods are used to extract the voltage waveform features obtained from non-contact voltage measurement. The identification results are used for early warning and processing of line faults. In this paper, aiming at the voltage monitoring system, the measurement accuracy and fault identification test are designed, and the steady-state average error is 0.9%, and the highest fault identification accuracy is 93%, which shows that the voltage monitoring system has high accuracy and fault identification accuracy.