## 1 Introduction
The machine learning method in artificial intelligence technology is the frontier of the research on DO prediction model of aquaculture water quality, and the machine learning method represented by SVR is able to characterize the water quality factors with multivariate, coupling, and nonlinearity, and can avoid the limitations of small samples, high dimensionality, overfitting, and local extremes of the traditional method, which has achieved a better application in the research of the DO content prediction in water quality [27]. In addition, the SVR-based cluster intelligent combination model is also an important research direction for the prediction method of DO in aquaculture water quality, but the choice of parameter combination optimization still lacks theoretical guidance [28]. Compared with the traditional genetic algorithm, ant colony algorithm, particle swarm algorithm, and other swarm intelligent optimization algorithms, the Differential Evolution (DE) and Gray Wolf Optimizer (GWO), as new swarm intelligent optimization algorithms, have the advantages of finding the global optimal solution in multi-dimensional space and dynamically adjusting the parameter weights adaptively. It has a broad application prospect in constrained optimization, cluster optimization, nonlinear optimization, continuous optimization, and combinatorial optimization [29,30]. The combined prediction method, based on the cluster intelligent optimization algorithm and SVR model, can better meet the demand for the accurate prediction of water quality DO content by using the complementary effect, which is an important research direction and an inevitable development trend for online water quality control in the aquaculture field. Therefore, this paper proposes a cluster intelligent combination model based on DE-GWO-SVR, which first uses the Pearson's correlation coefficient (PPMCC) to preprocess the water quality data of perch aquaculture, and then uses the DE algorithm to adaptively adjust the variation and evolution of the population in the GWO algorithm, so that the GWO algorithm is able to jump out of the trap of the locally optimal solution. Thus, the global search ability of the algorithm is improved. DE-GWO is combined into SVR model, and the optimal penalty factor C and the parameter gamma in the kernel function are obtained iteratively, so that the model can have better prediction accuracy and generalization ability. Therefore, the DE-GWO-SVR model is used to improve the prediction performance of the water quality DO content, which helps to realize the accurate online prediction and timely control of water quality DO content in aquaculture. In this paper, the key parameter, the DO value of water quality in high-density perch aquaculture ponds, was taken as the research object, and the water quality PH value, temperature, conductivity, salinity, and total organic salt content were collected as independent variables at the same time, and data correlation analysis was performed with the dependent variable DO respectively to determine the characteristic data, optimize the initialization parameter values of the model, and set up the cluster intelligent combination model based on DE-GWO-SVR, then compare with the SVR-based model and back-propagation neural network (BPNN) based model, and verify the reliability and stability of the DE-GWO-SVR model. The purpose of this paper is to combine the swarm intelligent optimization algorithm with the machine learning algorithm to accurately predict the DO content an aquaculture water body according to current water quality parameters. It can solve the problem that the DO content cannot be monitored in time due to the damage or absence of dissolved oxygen equipment in the process of high-density aquaculture, which leads to the anoxia of cultured fish.