According to a middle-season rice information decision tree classification method based on multi-temporal data feature extraction of the invention, selected GF-1 image data has the advantages of high spatial resolution and high temporal resolution. On the basis, a variety of characteristic parameters for rice distribution extraction based on a single-temporal image are used, the advantages of timing analysis based on a multi-temporal image are utilized, multiple parameters and multiple temporal phases are combined organically, and the distribution of middle-season rice is extracted by means of knowledge decision tree classification. Through use of a variety of characteristic parameters, non-target surface features can be eliminated better. Multi-temporal analysis is conducive to the elimination of wrongly-classified surface features caused by 'different surface features, same spectrum' and the extraction of target surface features. Decision tree classification has the characteristics of being flexible, visual, efficient, and the like. Therefore, by integrating all the advantages, the precision of middle-season rice extraction is further improved. The method is of positive significance both to the food security system of a country and to the commercial application of remote sensing in agriculture.