Missing data affects the quality of the data, which may lead to inaccurate results and reduce the reliability of the model. Missing value filling reduces the bias and facilitates subsequent analysis. Most missing value filling algorithms assume a weak correlation or even no correlation between multiple missing values, with little consideration of the correlation between missing values and the order of filling. Independent filling of missing values in the sales domain reduces the utilization of missing value information, which has a greater impact on the accuracy of missing value filling. To address the above problems, this study takes the sales field as the research objective and explores the updating mechanism of multiple missing values based on the multidimensional characteristics of sales behavior and the spatial distribution characteristics of output values of different models. In addition, the work studies the incremental filling method of multiple missing values of sales data, which is based on the correlation of features, orders the missing features, and fuses the already-filled data as an information element to incrementally fill in the following missing values. The algorithm also takes into account the generalization of the model. The algorithm takes into account the generalization of the model and the information correlation between the missing data and combines with multi-model fusion to effectively fill multiple missing values. Finally, the effectiveness of the proposed algorithm is verified by a large number of experimental comparisons based on a real-chain drugstore sales dataset.