As a critical research area in signal processing, speech enhancement plays a vital role in interpersonal communication, criminal investigation assistance, and military applications.To address limitations in existing deep learning approaches, including insufficient utilization of phase information and single-scale feature extraction leading to degraded speech quality, a monaural speech enhancement based on complex SepFormer and multi-scale convolution (CS-MSC) was proposed. To resolve the restricted feature diversity in conventional methods,a multi-scale feature extraction module was introduced to enhance the capture of both high- and low-frequency features and improve high-frequency detail reconstruction.Furthermore, skip connections with channel attention mechanisms were designed to mitigate information loss in deep networks and alleviate gradient vanishing. Additionally, complex-valued spectral representations were employed at the bottleneck layer to model amplitude-phase correlations, addressing the common neglect of phase information in conventional approaches.Finally, deepthwise convolution layers were incorporated into the encoder-decoder architecture to strengthen feature characterization along the frequency axis.Experimental results demonstrate that, compared to similar speech enhancement networks such as gated convolution recurrent network (GCRN) and deep complex convolution recurrent network (DCCRN), the proposed network significantly improves speech quality, intelligibility, and signal-to-noise ratio (SNR) under extremely low SNR conditions. Compared to DCCRN, the proposed network achieves improvements of 31.72% in perceptual evaluation of speech quality and 22.02% in composite quality measure on the VoiceBank-DEMAND dataset, effectively enhancing speech intelligibility and overall quality, and demonstrating notable robustness and generalization capabilities.