Recently, Vision Mamba (VIM) has garnered significant attention due to its promising capability in processing long visual sequences. Prevailing approaches typically adopt the Transformer architecture, substituting all MultiHead Attention (MHA) modules with Mamba blocks to enhance computational efficiency. However, such a direct replacement results in a scenario where, in deeper network layers, Mamba blocks operate on relatively short feature sequences, leading to computational costs (FLOPs) comparable to those of MHA and thus yielding limited efficiency improvements. Moreover, additional scanning strategies are frequently introduced to establish essential local spatial dependencies. To address these limitations, this paper investigates the efficient method of integrate visual state space models into vision backbone and introduces a novel model called EUMamba (Efficient Utilization of Mamba). Specifically, we propose a four-stage network design that allocates a greater number of Mamba blocks to stages with longer feature sequences, thereby maximizing their efficiency advantage in long-sequence processing and enhancing feature extraction capabilities. Concurrently, we design an overlapping convolutional module to explicitly capture local spatial relationships and systematically analyze the influence of kernel size on model performance. Furthermore, to augment the spatial understanding of state space models, we devise a spatial-frequency joint strategy, enabling Mamba to maintain a global receptive field throughout the scanning process. Experimental results demonstrate that EUMamba achieves competitive performance on visual benchmark tasks such as image classification.