MOTIVATION: Characterizing the neuronal connectomes provides route to understand the basis of neural circuit in brains, one of the central missions in neuroscience, but the mapped connectivity is absent of molecular information, obscuring the understanding on the important genes underlying the connectomes. The whole-brain spatial transcriptomics data provide the opportunity to predict and understand the brain connectivity. However, there is no method to process these datasets in consistent data format for integrative analysis. RESULTS: In this work, we developed a software to process different kinds of mouse brain connectivity data together with spatial transcriptomics in consistent brain regions to define the connectivity path and strength and then used the long short-term memory network to predict connectivity strengths from the spatial transcriptomics by using our data framework. We evaluated the model in different ways, and the results showed that our model accurately predicted the connectivity strengths and helped in selecting the important genes potentially involved in the regulation, establishment or maintenance of brain connectivity. AVAILABILITY AND IMPLEMENTATION: The software is freely available at Github (https://github.com/CPenglab/BrainConnect) and Pypi (https://pypi.org/project/BrainConnect). An archived version is available at https://doi.org/10.5281/zenodo.18440094.