Taking the entire county of Yongqing County, Langfang City, Hebei Province as the research area, the GF1-WFV 16-meter resolution image was used as the data source, and the image data covering multiple phases of the crop growth period was selected to construct the nonralized difference vegetation index (NDVI) time series of the crop. By analyzing the NDVI curve of the study area, it was found that the NDVI time series constructed with the data could describe the growth characteristics of crops in the study area, reflect the phenological differences of different crops in the region, and effectively distinguish the local planting patterns. The decision tree is constructed by selecting characteristic parameters such as the occurrence time of the maximum value, minimum value, peak, peak number and threshold value on the NDVI curve. According to the phenological calendar of the study area and the investigation of the local planting structure, the optimal time phase image was used to classify and extract one or several crops. Decision tree classification and neural network were used respectively, and accuracy verification was carried out to obtain the best crop classification method by comprehensive comparison. The results show that in the county research area of Yongqing County, when the GF1-WFV 16-meter resolution multi-temporal remote sensing data is used for crop classification, the accuracy of decision tree classification and neural network classification are 72.0729% and 87.3%, respectively. The optimal classification is obtained by using decision tree.