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In order to achieve rapid, accurate and dynamic classification of electrical accidents, an electrical accident classification model based on attribute and instance weighted naive bayes (AIWNB) is proposed. The prior probability and conditional probability in the naive Bayes classification method are improved by using two instance weighting methods. The eager instance weight depends on the statistical value of the frequency of each attribute value, and the lazy instance weight is determined by calculating the correlation between the training instance and the test instance one by one. Attribute weight is defined as the residual between attribute-attribute correlation and attribute-class correlation based on mutual information. The proposed AIWNB method organically combines attribute weighting and instance weighting in a unified framework of Naive Bayes, use the electrical measurement data of high and low voltage users to verify. Experimental results show that compared with pure Naive Bayes, the weighted Naive Bayes is more competitive, and the accuracy and F1 score can be increased by 3.09% and 9.39%, which proves the prac-ticality and effectiveness of the algorithm in the classification of electrical accidents, and the proposed method can be extended to other classification situations.