Faced with the serious water environment problem, the efficient management of water resources is the key way to solve this problem, and the state assessment of blooms has become a top priority. Aiming at the non-linearity and imprecision problems in the evaluation of algae blooms in rivers and lakes, an artificial neural network based algae bloom state evaluation model was constructed, and the effective integration of fixed site monitoring and remote sensing information was realized. The model was applied to the evaluation of algae blooms in Taihu Lake. The evaluation results were in accordance with the actual situation, which verified the validity and feasibility of the model, and provided the idea for the further study of algae bloom.