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In order to promote the informatization development of offshore aquaculture, realize the monitoring of offshore aquaculture environment more accurately and conveniently, and solve the problems of poor prediction accuracy and robustness of traditional offshore aquaculture water quality prediction methods, an environmental monitoring system was designed based on buoy platform, which realized the remote collection and data storage functions of multi-regional environmental information monitoring data. On this basis, an improved genetic algorithm was proposed to optimize the offshore dissolved oxygen prediction model of BP neural network to realize the prediction of offshore aquaculture environment. The STM32L475 microcontroller was used to collect information such as illumination, temperature, pH value, dissolved oxygen and so on with the help of sensor network, and transmitted the data to the cloud monitoring platform through the Internet of things technology, thus realizing remote monitoring of multi-regional environmental information and multiterminal access. Through the analysis and research of classical prediction algorithms, a dissolved oxygen prediction model based on traditional algorithm optimization was proposed to realize the accurate prediction of offshore aquaculture water quality environment. According to the collected data of aquaculture environment, the initial weights and thresholds were optimized by improved genetic algorithm to obtain the optimal parameters, and then the BP neural network dissolved oxygen prediction model was constructed. Through experiments, the accuracy and reliability of marine environmental information collection and the effectiveness of dissolved oxygen prediction model were verified. Compared with the traditional neural network prediction model, the average error was reduced from 0.0778mg/L to 0.0178mg/L, which can meet the actual needs of offshore aquaculture.