【Objective】 Predicting and screening potential pesticide-contaminated sites timely and efficiently is important for controlling environmental pollution. 【Method】 Based on 46 pesticide sites samples published on the World Wide Web, the index system and method for rapid prediction of soil pollution in pesticide sites was established by a five-score analytic hierarchy process. 【Result】The predictive system was constituted with four factors: product characteristics, local climatic conditions, soil properties and site characteristics, including 11 characteristic indicators: product toxicity,product persistence, average annual temperature, average annual precipitation, average annual wind speed, light, soil texture, pH, organic matter content, production time and idle time. There is a significant linear correlation between the three indicators: production time level, product toxicity and durability level, and the soil pollution of the pesticide site. The linear comprehensive prediction accuracy of the three indicators is less than 65%. Also, the comprehensive judgment accuracy of the machine (SVM, BP) learning method combining 11 indicators is 82%, but all of them have significant limitations as they missed classified the severity of the contaminated sites. 【Conclusion】The comprehensive evaluation index value P≥0.6 is used as the prediction threshold of soil pollution in pesticide sites. The accuracy of the comprehensive prediction of the five score AHP is 91%, which is better than linear prediction and the machine learning method. It has the characteristics of low demand for key data, and efficient , and reflects the principle of“Implemented to the strictest standards without leaving a contaminated site”. It can be used for pre-diagnosis of soil pollution in various types of pesticide sites.