The intermittent fault of the electrical connector is a common fault type in equipment, and its signal performance has a strong correlation with the degradation state of electrical connector and the environmental stress level. It is difficult to evaluate the degradation state of electrical connector. To address this issue, a new method is proposed in this article. The dynamic characteristics of intermittent fault signals of electrical connectors under sinusoidal vibration conditions are analyzed, and the behaviors of relative displacement between the contact interfaces obtained from dynamic model analysis reflect that the intermittent fault’ s bimodal amplitudes and their time delay are effective parameters to evaluate the degradation state of electrical connectors. The characteristic parameter dataset is constructed. Furthermore, a state assessment model based on deep belief network and multitask learning is formulated. The loss function of the model is improved as the iteratively weighted summation of the partial losses. Based on the parameter dataset of intermittent fault, the degradation states of electrical connectors are evaluated and analyzed, and the accuracy reaches 95. 94% . The proposed method provides a new research route for the degradation state assessment of electrical connectors.