Abstract: A data analysis method for optimization of aluminum anodizing and dyeing process acquires a plurality of sample data groups, each sample data groups comprising dyeing result data and parameter data of multiple processing parameters. Contribution value of each processing parameter data relative to the dyeing result data in each of the plurality of sample data groups is determined, and the contribution values are used to determine at least one essential processing parameter. The essential processing parameters are then adjusted according to a data analysis result for improving quality of products. A computing device and storage medium are also provided.