Underwater object detection is a key technique for marine research. Traditional red green blue (RGB) imaging suffers from short detection ranges and serious color distortion in complex underwater environments. While hyperspectral images contain detailed spectral information, their high dimensionality increases computational costs and noise vulnerability, and high-quality underwater samples are difficult to obtain. A continuity and label-constrained band selection (CLBS) method is presented to address three common defects of existing underwater band selection techniques: target–background confusion, underutilization of spatial features and poor spectral continuity. Three metrics, namely target–background contrast (TBC), target region entropy (Entropy) and spectral–spatial contrast (SSC), are designed for band evaluation. Geometric mean fusion is adopted to suppress extreme values, and spectral continuity constraints are applied to determine the optimal band number. Meanwhile, a multi-category underwater hyperspectral dataset is constructed. Quantitative experiments are carried out on two fully annotated classes (metal and plastic). CLBS reduces the 300 original bands to 209, delivering a 30.3% dimensionality reduction with well-preserved spectral continuity. On a fixed train-validation partition, the 3DCNN+2DCNN model using CLBS-selected bands reaches an F1-score of 94.29%, a Precision of 100.00% and a Recall of 89.19%. Ten repeated tests with random seeds yield averaged results of 94.96 ± 1.13%, 98.44 ± 1.65% and 91.82 ± 3.31% for F1-score, Precision and Recall, respectively, demonstrating reliable performance. Comparative results show that CLBS outperforms conventional feature extraction and various supervised/unsupervised band selection methods. It achieves an excellent trade-off between dimensionality reduction and spectral feature preservation for underwater hyperspectral image processing.