This study presents a novel approach aimed at addressing the detection and recognition challenges of underwater Unmanned Underwater Vehicle(UUV) radiation noise. The proposed method combines Adaptive Feature Modal Decomposition (AFMD) with a Joint Convolutional Neural Network (JCNN). Initially, the AFMD is applied to the signal to obtain a set of decomposition components, followed by reconstruction based on the GINI index to select the most optimal component. Subsequently, the reconstructed signal undergoes continuous wavelet transformation, producing 2D time-frequency maps representative of distinct radiation noise categories. The methodology incorporates a feature fusion module within the Frequency Dynamic Convolution Module and SGE Module to establish the JCNN. This network adeptly extracts features from the 2D time-frequency maps, facilitating the classification of UUV radiation noise. Experimental findings demonstrate the method"s effectiveness in accurately identifying underwater UUV radiation noise, achieving a notably high recognition rate.