he messages sent by the data link often carry tactical information. In this paper, the specific content of the message being unknown, the feasibility of cognizing typical data link tactical behaviors is experimentally explored. Firstly, according to the characteristics of formatted messages, the structure of the V-series messages with a tactical message is simulated, and simultaneously, the modulated signal is of data link generated. And then the wavelet packet decomposition method is utilized for extracting the time-frequency features and constructing the time-frequency graph datasets. Deep learning is used to classify the signals containing different tactical messages, and the attention mechanism module CBAM applied to CNN is introduced to further improve the recognition accuracy.The experiments prove that even if the message structure is unknown, the corresponding tactical task can be recognized by identifying the physical layer signals containing different V-series tactical messages. Finally, based on the experimental results, a new data link tactical behavior reconnaissance process is proposed.