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Fish mass is crucial for evaluating fish growth status, promoting precise feeding in aquaculture, and improving aquaculture efficiency. To accurately estimate fish mass, a fish mass estimation method based on dual dimensional feature extraction and natural gradient boosting （NGBoost）was proposed under the premise of using binocular cameras. Firstly, fish images were obtained through a binocular camera, and camera calibration and image correction operations were performed. Secondly，image processing technologies were used to segment the corrected image to obtain the fish target, and the two-dimensional features of the fish target were extracted. On this basis, stereo matching was performed to obtain the fish disparity map, extract the corresponding key matching points of the left and right images of the fish, and calculate the coordinates of the three-dimensional spatial feature points by using the triangular transformation principle, achieving the extraction of the three -dimensional features of the fish target. Finally, the method based on NGBoost was used to predict fish mass. Different dimensional features of fish from two - dimensional plane and three - dimensional space were extracted, solving the problem of inaccurate prediction of fish mass caused by single -plane dimensional features. At the same time, in addition to common three -dimensional features such as length and width, the fish depth ratio was also extracted, enriching the feature representation of the model and improving the accuracy of fish mass prediction. The crucian carp were taken as the experimental object and the proposed method was tested on the real dataset. The results showed that the mean absolute error（MAE）was 0.006 3 kg, the root mean square error （RMSE）was 0.008 7 kg, and the coefficient of determination（R2）was 0.928 7.Compared with various mass estimation methods, the performance of each evaluation metric of the proposed method has been greatly improved, predicting the fish mass more accurately.