The　monitoring　of　harmful　phytoplankton　is　very　important　for　the　maintenance　of　the　aquatic　ecological　environment.　Traditional　algae　monitoring　methods　require　professionals　with　substantial　experience　in　algae　species,　which　are　time-consuming,　expensive,　and　limited　in　practice.　The　automatic　classification　of　algae　cell　images　and　the　identification　of　harmful　phytoplankton　images　were　realized　by　the　combination　of　multiple　convolutional　neural　networks　(CNNs)　and　deep　learning　techniques　based　on　transfer　learning　in　this　work.　Eleven　common　harmful　and　31　harmless　phytoplankton　genera　were　collected　as　input　samples;　the　five　CNNs　classification　models　of　AlexNet,　VGG16,　GoogLeNet,　ResNet50,　and　MobileNetV2　were　fine-tuned　to　automatically　classify　phytoplankton　images;　and　the　average　accuracy　was　improved　11.9%　when　compared　to　models　without　fine-tuning.　In　order　to　monitor　harmful　phytoplankton　which　can　cause　red　tides　or　produce　toxins　severely　polluting　drinking　water,　a　new　identification　method　of　harmful　phytoplankton　which　combines　the　recognition　results　of　five　CNN　models　was　proposed,　and　the　recall　rate　reached　98.0%.　The　experimental　results　validate　that　the　recognition　performance　of　harmful　phytoplankton　could　be　significantly　improved　by　transfer　learning,　and　the　proposed　identification　method　is　effective　in　the　preliminary　screening　of　harmful　phytoplankton　and　greatly　reduces　the　workload　of　professional　personnel.