In order to solve the problem of low node positioning accuracy caused by complexity of underwater wireless sensor network environment and node dynamics, a node movement prediction and positioning algorithm based on dynamic Bayes LS-SVM was proposed in this paper. In this algorithm, the distance and hop matrix from the beacon node to all beacon nodes within the communication radius are used as the training set, and the normalization process is carried out. The Bayesian LS-SVM model was constructed using Bayesian evidence framework, and the hop number vector between the unknown node and the beacon node was taken as the test set to determine the distance between the node and the beacon node. Then the equation of the distance matrix between the node and the beacon node was established and the maximum likelihood estimation method was used to estimate the coordinates of the unknown node. Finally, all unknown nodes were located by iterative method, and the adaptive increment and subtraction algorithm was used to dynamically adjust the model parameters and prediction model to adapt to the dynamic changes of data. The experimental results show that the average positioning error of the algorithm is reduced by 24. 77%, 22. 25%, 3. 1%, and 6. 5% compared with the SLMP algorithm, RTLC algorithm, NDSMP algorithm, and MPL algorithm under the same node density, effectively realizing underwater positioning. Dynamic positioning of unknown nodes.