With the continuous development of network attack methods, it is becoming increasingly difficult to protect the security of power communication networks. Currently, the detection accuracy of abnormal traffic in distribution communication networks is insufficient and the efficiency of abnormal traffic detection is low. To address these issues, a new method for abnormal traffic detection in distribution communication networks is proposed, in which feature extraction and traffic classification are improved. The proposed method utilizes a time-frequency domain feature extraction method, using an adaptive redundancy boosting multiwavelet packet transform to quickly extract frequency-domain features, while time-domain features are extracted using the communication characteristics of the distribution network. To improve traffic classification and detection, a parallel deep forest classification algorithm is proposed based on a distributed computing framework, and the training and classification task scheduling strategies are optimized. The experimental results show that the false alarm rate of the proposed method is only 2.63% and the accuracy rate for the detection of abnormal traffic in distribution networks is 98.29%.