The invention discloses a retail store product sales predicting method based on collaborative filtering. The sales prediction of unknown products (specifications) in retail stores is worked out based on the collaborative filtering technology according to sales data of known products (specifications) in different retail stores. According to the scheme, the method comprises the steps that firstly, data such as the retail stores, the products (specifications) and the known sales of the products in the retail stores are collected; secondly, sales-grade conversion is carried out on the known sales to obtain the corresponding grading value; thirdly, the retail stores correspond to users, the products correspond to projects, the grade is the sales-grading conversion result obtained in the second step, and unknown grades are estimated according to the collaborative filtering algorithm; fourthly, the sales-grade inverse transformation is carried out on the grade estimation worked out in the third step, and then the sales prediction is obtained. As the improvement, six kinds of sales-grade conversion and the corresponding inverse transformation are provided, and therefore the sales data of different products in the different retail stores have the comparable grading numerical values with the uniformed standard for the collaborative filtering computing. According to the designed predicting method, the enterprise accurate marketing can be achieved, and guidance can be provided for the marketing activity development and estimation of the products in the retail stores.