Visible and near infrared reflectance spectroscopy is an effective method for the quantitative estimation of soil available phosphorus content.However,when the spectral data collected from the soil of one region are used for those of other regions,problems such as low prediction accuracy and model failure may occur.In order to solve these problems,a prediction model through transfer learning method was established in this paper,with soil samples from southern Anhui province as the source domain and soil samples from northern Anhui province as the target domain to improve the accuracy of soil available phosphorus model prediction.By comparing the prediction accuracy of the model before and after the transfer,it was found that the model for southern Anhui region could not be directly used for Northern Anhui region,in that case,model failure may occur.The coefficient of determination(R2) and the ratio of prediction to deviation(RPD) of the model were-0.19 and 0.92,respectively,and the root mean square error of prediction(RMSEP) of the model was 1.04.The predictive accuracy of modeling for the North Anhui region with a small sample size was low,with R2 and RPD of 0.61 and 1.60,respectively,and RMSEP of 0.60.Based on the transfer component analysis(TCA) and adding some samples from northern Anhui to build the model,the prediction accuracy of the model for available phosphorus in samples from northern Anhui was significantly improved,with the R2 and RPD improved to 0.79 and 2.18,respectively,and the RMSEP reduced to 0.44.Results showed that the prediction model based on TCA method for soil available phosphorus in southern Anhui region could be applied to that in northern Anhui region,improving the prediction accuracy and reducing the modeling cost,and it provides a new idea for the wide application of prediction model for soil available phosphorus.