The efficacy of time-delay neural networks (TDNN) in speaker recognition has been demonstrated. ECAPA-TDNN builds on TDNN, improving performance levels at the cost of increased computational complexity and slower inference speed. However, the effectiveness of ECAPA-TDNN does not meet the expected standards for speaker recognition in complex scenarios. This motivates us to seek an architecture superior to ECAPA-TDNN. In this paper, we propose an efficient network called SC-EcapaTdnn, which is a fusion of separable convolutions and ECAPA-TDNN. This innovative design uses ECAPA-TDNN as the backbone, uses depth-separable convolution blocks to encode acoustic features, and generates high-resolution frequency feature maps, allowing the backbone model to obtain more refined and effective speaker features. At the same time, we replace the squeeze excitation (SE) module in ECAPA-TDNN with adaptive one-dimensional convolution to generate channel attention weights to extract inter-channel dependencies. Ultimately, we trade a small increase in model parameters for a significant increase in performance. Training on the AISHELL and CN-Celeb datasets shows that our proposed architecture outperforms other mainstream speaker recognition systems.