The brain structures are key indicators to represent the complexity of many cognitive functions, e.g., visual pathways and memory circuits. Inspired by the topology of the mouse brain provided by the Allen Brain Institute, whereby 213 brain regions are linked as a mesoscale connectome, we propose a mouse-brain topology improved evolutionary neural network (MT-ENN). The MT-ENN model incorporates parts of biologically plausible brain structures after hierarchical clustering, and then is tuned by the evolutionary learning algorithm. Two benchmark Open-AI Mujoco tasks were used to test the performance of the proposed algorithm, and the experimental results showed that the proposed MT-ENN was not only sparser (containing only 61% of all connections), but also performed better than other algorithms, including the ENN using a random network, standard long-short-term memory (LSTM), and multi-layer perception (MLP). We think the biologically plausible structures might contribute more to the further development of artificial neural networks.