The species diversity and community structure of planktonic algae are important appraisal indicators for evaluating aquatic ecological environment construction, and the recognition of phytoplankton by cell image is a crucial way to achieve the detection of phytoplankton. Compared with the conventional microscopic detection method, the target detection algorithms based on deep learning have been increasingly employed in planktonic algae detection because of their effective detection capability. Aiming at the low detection accuracy challenges of small shape, fuzzy boundary, and cohesive planktonic algae in the YOLOv3 target detection algorithm, spatial pyramid pooling (SPP) was employed to enhance the feature extraction method of the YOLOv3 target detection algorithm. Additionally, the generalized intersection over union (GIoU) boundary loss function was employed to replace the YOLOv3 target detection algorithm in this study. Finally the SPP-GIoU-YOLOv3 planktonic algae detection algorithm was constructed based on the YOLOv3 algorithm. The findings demonstrate that the mean average precision of the SPP-GIoU-YOLOv3 target detection algorithm for detecting planktonic algae is up to 95.21%, which is 4.24 percentage points higher than that of theYOLOv3 algorithm. These findings are important for developing accurate rapid detection methods and technologies of planktonic algae.