In response to the problem that background interference leads to insufficient extraction of key features of underwater targets and the attention area cannot cover the entire target, an underwater target detection algorithm with mixed attention and dynamic feature guidance network was proposed. This method first designs a full-dimension dynamic extraction module （FDEM） to fully understand the overall features of the target. Secondly, a weighted feature concatenation module (WFCM) is designed to retain key features from the shallow to the deep layers. The CARAFE operator is used for the feature up-sampling operation, so that the key features of local areas can receive more attention. Finally, a hybrid attention mechanism (HAM) is constructed to effectively combine the channel information and pixel information of the target and retain the key features. Experimental verification shows that this method can fully extract key features, pay more attention to the overall features of the target, thereby reducing underwater background interference and further enhancing the underwater target detection performance.