| Summary: | Black soil plays an important role in maintaining a healthy ecosystem, promoting high-yield and efficient agricultural production, and conserving soil resources. In this paper, a typical black soil area of Keshan Farm in Qiqihar City, Heilongjiang Province, China, is used as a case study to investigate the black soil farmland productivity evaluation model. Based on the analysis of the composite index (CI) model, productivity index (PI) model and various machine learning models, the soil productivity evaluation method was improved and a prediction model was established. The results showed that the support vector machine regression model based on simulated annealing algorithm (SA-SVR), as well as the Gaussian process regression model (GPR), had obvious advantages in data preprocessing, feature selection, and model optimization compared to the modified composite index model (MCI), the modified productivity index model (MPI), and the coefficients of determination (R<sup>2</sup>) of their modelling, which were up to 0.70 and 0.71, respectively, and these machine learning prediction models can reflect the effects on maize cultivation and its yield through soil parameters even with small datasets, which can better capture the nonlinear relationship and improve the accuracy and stability of yield prediction, and is an effective method for guiding agricultural production as well as soil productivity evaluation.
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