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• The use of multi-scale convolutional layers can effectively help to identify hidden dependencies between multiple sequences, capture local patterns in the input sequences more flexibly, and extract location-specific features at different levels. • As global response normalization can achieve global feature aggregation, it can help extract more accurate features in the model and fully express the key information of the 6mA site. • The prediction results are better than other models, and a vector of contribution scores is created that clearly explains the prediction mechanism. DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results. [ABSTRACT FROM AUTHOR]