ABSTRACT To address the poor network security protection and low attack traffic identification, the study designs a network security monitoring system based on convolutional neural network and exponential weighted Dempster Shafer evidence theory. The validation of the UNSW-NB15 data set showed that the output of multi-source fusion after exponential weighted Dempster Shafer evidence theory was higher than the output of the feature fusion by 3.92%. The accuracy of the attack recognition was as high as 93.72%, which was 1.85% higher than feature fusion. The accuracy of the proposed network security monitoring system increased by 3.70% on average over other methods. The results indicate that the proposed network security monitoring system can effectively improve the efficiency of network attack identification, monitor network security in real-time, and effectively protect network operation. The system is feasible and reasonable in terms of network security situational awareness, which can provide effective situational analysis for network administrators.