MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as Alzheimer's disease (AD). Although existing drugs can temporarily alleviate symptoms, none have been proven to delay or prevent disease progression. Acetylcholinesterase inhibitors (AChEIs) have been shown to mitigate AD symptoms, yet traditional AChEI screening approaches remain time-consuming and inefficient. RESULTS: To address this limitation, we developed multi-species AChEI screening network (MAISNet), an AChEI screening framework based on acetylcholinesterase (AChE) data from six species. In MAISNet, inhibitor molecules were represented as SMILES-derived molecular graphs, whereas AChE protein structures were encoded as residue contact maps. Multi-scale molecular and protein features were extracted using the sample and aggregate (GraphSAGE) network and the graph attention network, respectively, and were subsequently fused through a bidirectional cross-attention mechanism. The integrated representations were then processed by a multilayer perceptron (MLP) to inhibitor classification. On both internal and external validation sets, MAISNet consistently outperformed five baseline models. Furthermore, we applied MAISNet to screen existing small molecules, and Methyl 2-[(3S)-3-(1, 2, 3, 4, 5, 6, 7, 8-octahydro-2-naphthyl)-2-(methoxycarbonyl)-1H-pyrrol-1-yl]acetate subsequently emerged as the top-ranked candidate. Overall, MAISNet significantly improves the accuracy and generalization capability of AChEI screening, providing an efficient and reliable computational tool for accelerating therapeutic discovery for AD. AVAILABILITY AND IMPLEMENTATION: Code that supports the reported results can be found at: https://github.com/liangshengjie111/MAISNet. The archival version of the code is preserved on Zenodo at https://doi.org/10.5281/zenodo.18721665.