In order to improve the bait weighing accuracy of the discharging device under the swaying and bumping conditions at sea, a weighing error correction algorithm based on PSO-BP neural network is proposed. Based on the sensing technology, a closed experimental device with load cell and attitude sensor was established, and the weighing and attitude data in the tilt range of 0-20 degrees under different counterweights were measured in the offshore farm, and after determining the correction coefficients, the BP neural network algorithm was introduced to obtain the predicted values of the weighing. In the test samples with real masses of 8.06 kg and 12.4 kg, the maximum relative errors of the weighing data corrected by the BP neural network algorithm were reduced by 4.32% and 4.36%, respectively, compared with the direct measurement method; the maximum relative errors of the weighing data corrected by the PSO-BP neural network algorithm were reduced by 0.39% and 0.33%, respectively, compared with the BP neural network algorithm. The maximum relative errors of the PSO-BP neural network algorithm were reduced by 0.39% and 0.33%, respectively. The PSO-BP neural network algorithm has a higher accuracy in error correction for bait weighing in offshore net-pen aquaculture.