To address the non-intrusive requirement of measuring arcing time of low-voltage circuit breakers, it′s crucial to overcome the interference of strong acoustic events such as mechanical collisions of opening sound signal on the identification of weak arcing acoustic events as well as the difficult identification of arcing sound signals′ start and end boundaries. Thus an arcing time measurement method based on the characteristic frequencies of acoustic-electric field signals is proposed. First, the acoustic signal segments corresponding to the arcing stage are obtained according to the division results of acoustic events during the complete opening process of the circuit breaker. Then, a kurtosis-permutation entropy index is constructed as the fitness function of bitterling fish optimization-based variational mode decomposition, which is used to adaptively decompose the acoustic signal segments. Combined with the characteristic frequency of arcing acoustic events obtained from power spectrum analysis and correlation coefficient criterion, effective modal components are selected. These components are then denoised with the singular value decomposition and reconstructed to suppress mechanical collision interference and highlight arcing components. Then a band-pass filter is designed based on the frequency characteristics of electric field signal to extract the very low-frequency components, thereby improving the distinguishing ability of arcing events′ boundaries. Taking the reconstructed acoustic signal and the very low-frequency electric field signal as inputs, a one-dimensional convolutional neural network based binary classification model is built for the arcing events. the model outputs the event probability of arcing duration, which exhibits the high precision and recall performance. To verify the effectiveness of proposed method, tests were conducted at different phase breaking current conditions. The results show that the mean absolute error, mean squared error, and root mean squared error do not exceed 0.25. Furthermore all indicators are improved by more than 76.2% compared with other measurement methods. In conclusion the proposed method possesses the high measurement accuracy and robustness, which provides the potential application value of non-intrusive online condition monitoring of low-voltage circuit breakers.