With the rapid development of object detection technology, underwater object detection has gradually become a hot topic. Due to the complex underwater environment, some object detection algorithms still encounter difficulties in detecting targets similar to the background. For the above problems, we propose a multi-scale feature-weighted dual-neck network (MFDNN) for underwater object detection. Our contribution is mainly divided into three parts. First, an enhanced feature extraction network, namely the dual-neck network, is designed to process and reuse the features extracted from the backbone network. Second, an attention mechanism is embedded in one of the neck networks to reweight features and pay more attention to important features. In addition, we introduce the adaptively spatial feature fusion mechanism to adaptively weight the features extracted at multiple scales. As demonstrated in comprehensive experiments, the mean average precision of our MFDNN can reach 87.79% and 86.51% on the underwater datasets URPC2019 and URPC2020, respectively.