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Chrysanthemums have a wide variety of flower types with subtle differences in flower phenotypes, which are difficult to label accurately, and this poses a great challenge for intelligent classification and recognition of chrysanthemums. Based on deep active learning and hybrid attention mechanism module, i.e. convolutional block attention module (CBAM), a method and framework for intelligent recognition of chrysanthemum phenotypes under insufficient labeling data was proposed. Firstly, the more informative samples among the unlabeled chrysanthemum samples were selected for labeling by an active learning strategy based on the optimal labeling and second-optimal labeling method BvSB (Best vs second-best), and the labeled samples were put into the training samples;secondly, a deep convolutional neural network ResNet50 was used as the backbone network to train the labeled samples, and the hybrid attention mechanism module CBAM was introducted, so that the model can more accurately extract the high-level semantic information in fine-grained images;finally, the classification model continued to be trained with the updated training samples until the model reached the number of iterations and then stopped. The experimental results showed that the method can achieve 93.66%, 93.15% and 93.41% of precision, recall and F1 value respectively with a small number of chrysanthemum labeled samples. The method can provide technical support for intelligent identification of chrysanthemums and other flowers under the situation of insufficient labeling data.