The distribution of phytoplankton classes in freshwater in imbalanced,with collected microscopic images containing significantly more samples of advantaged classes than of disadvantaged items. General deep-learning-based image classification methods trained on such datasets generally perform poorly in classifying disadvantaged classes. In addressing the classification errors caused by the class-imbalanced phytoplankton dataset in deep learning model,various solutions for handing this issue in macro-domain have been analyzed. The practicality of these methods in the domain of microscopic images of phytoplankton is explored. A dataset consisting of 29 genera and 18044 images from Lake Chaohu was collected,constructing a microscopic image dataset of phytoplankton with class-imbalanced problem. An evaluation of the model's classification abilities was proposed using both micro-average and macro-average metrics. Experimental results indicate that the model trained by general method performs lower F1 values when predicting samples from disadvantaged classes. Conversely,the model trained by the square-root sampling method in the re-sampling major category exhibit significant improvement in both micro-average and macro-average metrics,with F1 values reaching 0.932 and 0.852,respectively. Particularly,on the top 10 disadvantaged genera,the F1 values for micro-average and macro-average increased by 9.64% and 15.94%,respectively. This study provides an effective method for training deep learning model for the automated detection of phytoplankton community structure in freshwater.