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Recognition of target attack intent in naval ship air defense and antimissile operations is a hot research topic in naval ship air defense technology. The attack intention identification of incoming targets is an important part of battlefield situation analysis. In the past, quantitative analysis was conducted through prior knowledge and probability, and the impact weights of attack intention identification feature values were determined. Deep neural networks can adaptively learn the eigenvalues of target attack intentions, and can learn the correspondence and mapping between feature data and attack intention recognition through eigenvalue training of target attack intentions in small sample sets without prior knowledge. By introducing the GeLUs activation function and the AdaMod optimization algorithm, the model convergence is accelerated, and the problem that the Adam model may not converge to the optimal solution is solved. Experimental results of the text show that the model proposed can effectively identify the attack intent of incoming targets with insufficient prior knowledge and small training data, while ensuring a higher accuracy rate.