Network security visualization technology makes use of human vision's ability to obtain models and structures, and presents abstract network and system data in the form of graphics and images, which is very important in the research of video and image recognition. The purpose of this study is to apply the technology of network security data visualization fusion in image recognition and analyze its role. In this study, Visda-2017 and other data sets are used, and image data preprocessing, image dehumidification, registration and fusion are carried out. After setting the parameters, the parameters are trained by convolution neural network. Finally, the network situation value and the accuracy and false alarm rate of this algorithm are calculated. The results show that when the threshold value δ1 = 2, it has a high detection rate, with an average rate of 98%, and a high false alarm rate of 1.6%; and the image recognition accuracy rate of the classifier of F-CDCGAN model is the highest among the six methods, reaching 99.46%. It is concluded that the image recognition algorithm combined with network security visualization technology fusion, feature points extraction and recognition accuracy and detection rate is high, false alarm rate is low. This study provides an effective method for data security visualization fusion image processing and recognition.