Abstract: Abstract: Ammonia nitrogen concentration is an important parameter to evaluate the quality of aquaculture water, and it determines the yield and benefits of intensive aquaculture production. In order to solve the problems of high cost, high consumption and difficulty in real-time and effective detection of ammonia nitrogen concentration, a method combining bagging ensemble algorithm and stochastic configuration network (SCN) which called Bagging-SCN were proposed. In this method, according to the current development of ammonia nitrogen measurement methods and random neural networks technology, SCN was chosen as the base learner due to its advantages of fast learning speed and strong ability to approach training data. The bagging ensemble method was used to integrate multiple networks, which effectively reduced the variance of the integrated model under the condition of keeping the model deviation unchanged. Specifically, the bootstrap method was used to generate multiple different training subsets for parallel training of multiple SCN models, and then different SCN models were generated by training with different subsets, and the uncollected samples in this subset were used as the verification set of each base SCN model to verify the performance of each model. Finally, the outputs of all base SCN models were averaged as the output of the final model, and the test set was used to evaluate the final model. In the modeling process of base learners, the SCN model started from a small network with little human intervention and randomly selected input weights and thresholds based on inequality constraints. It adaptively selected the value range of the random parameters according to the size of the random parameters to further ensure the universal approximation of the randomized learning model. The bagging method solved the problem that the randomization of network parameters and the uncertainty of network structure lead to the instability of measurement effect in the process of SCN modeling, and improved the measurement accuracy and stability of the model. To verify the validity of the proposed method, the experiments were mainly performed using two data sets with different backgrounds. The first experiment was based on the airfoil self-noise data set in the UCI standard database, and the frequency, angle of attack, chord length, free-stream velocity, and suction side displacement thickness was chosen as the auxiliary variables for modeling of scaled sound pressure level. The soft sensing modeling method of Bagging-SCN, SCN, random vector functional link net (RVFL) and Bagging-RVFL were carried out respectively based on the data set, for 20 consecutive times, and the output results of each model were statistically analyzed. These algorithms were verified by comparing the mean of the root mean square error (RMSE), the mean of the maximum absolute error (MAE) and the mean of the average absolute percentage error (MAPE) of the output predicted by different models, and the experimental results showed that the proposed Bagging-SCN model had a certain improvement in measurement accuracy and stability and had the best measurement performance compared with other models. The data set in the second experiment was collected by our laboratory intensive aquaculture system, and the proposed method was applied to the soft-sensing of ammonia-nitrogen concentration in intensive aquaculture. The relevant water quality parameters such as water temperature, pH, dissolved oxygen, conductivity which collected by sensors in the laboratory system were used as auxiliary variables for modeling of ammonia nitrogen concentration. Experiments with comparisons on the prediction effect of Bagging-SCN, SCN, Bagging-RVFL and RVFL models were carried out as the first experiment. Results indicated that the proposed algorithm had higher prediction accuracy and better generalization performance when measuring the ammonia nitrogen concentration in intensive aquaculture water. It had certain guiding significance for the monitoring of aquaculture water bodies.