Abstract: Establishing fish growth and development models based on big data and artificial intelligence technology to achieve fine management has become a hotspot in the aquaculture industry. This article introduced the data preprocessing methods such as the source and collection of pike perch farming data and qualitative feature processing, then the steps of dimensionality reduction using factor analysis and principal component analysis, and finally the method of dividing the machine learning data set and the steps of using BP neural network, support vector machine, XGBoost to establish the growth model of pikeperch. The analysis of the established models showed that the models based on randomly divided data sets, BP neural network and principal component analysis are the best predictors for the growth and development of pikeperch.

Key words: Pike perch, growth model, machine learning, data preprocessing, impact factor