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The electromagnetic environment of modern battlefield is becoming more and more complex, and the tactical communication network stations mainly focus on the ultra-short wave frequency band. The future technical reconnaissance has an increasingly strong support demand for intelligent and big data processing. In order to realize the classification of ultra-short wave blind signals, a classification method combining the time spectrum of blind signals with the optimized VGG16 network is proposed. The method first converts the actual VHF blind signals collected in the electromagnetic battlefield into time-spectrum maps, then combines them with the optimized VGG16 convolutional neural network through transfer learning, and introduces the cavity convolution into the network to complete the classification of VHF blind signals. Experimental results show that the optimized VGG16 network has a higher accuracy than the original network, reaching 93.1%. When the cavity convolution is introduced into the 7th and 10th layers of the optimized VGG16 network, the recognition rate reaches the highest of 92.2%, and the learning time is reduced by 34.1%, which greatly reduces the training time of the model, and verifies the effectiveness of cavity convolution in VGG16 blind signal classification and recognition.