With the rapid development of battlefield communication reconnaissance countermeasure system, communication signal system becomes more complex, which brings difficulties to communication signal detection, modulation recognition and signal emitter individual recognition under non cooperative reception conditions. In order to fully grasp the prior information of the signal, blindly detect and identify the complex communication signal system, this paper proposes an automatic modulation recognition method for multiple communication signals based on time-frequency diagram analysis and deep neural network. Through time-frequency analysis, different typical communication signals are firstly converted into time-frequency images. Then the marked time-frequency diagram is input into YOLOv6 network based on deep learning for feature learning. By designing a more efficient network structure, YOLOv6 can quickly identify the time-frequency diagram of signals. Based on the generated network weight, the typical communication overlapping signal is tested, the extracted feature vectors are classified and recognized, the identification of six modulation modes and the rapid determination of the position is completed. Finally, the detection and recognition of multiple typi-cal communication signal modulation modes under the condition of non-cooperative reception are realized.