In this study, count-based Morgan fingerprints (CMF) were employed to represent the fundamental chemical structures of contaminants, and a neural network model (R² = 0.76) was developed to predict acute fish toxicity (AFT) of organic compounds. Models based on CMF consistently outperformed those based on binary Morgan fingerprints (BMF), likely due to the latter's inefficiency in describing homologous structures. The similarity of CMF was calculated using an improved method based on Tanimoto distance, which was used for calculation of dataset partition and application domain. The similarity-based dataset partitioning method ensured structural diversity within the training set and improved performance on the validation set, demonstrating its potential for toxicological structure analysis and priority screening. Toxic substructures identified by Shapley additive explanation (SHAP) method were substituted benzenes, long carbon chains, unsaturated carbons and halogen atoms. By incorporating K