Currently, super-resolution reconstruction technology is applied in various fields. However, digital elevation model (DEM) reconstruction presents numerous challenges. To address the issues of detail loss and distortion caused by inadequate utilization of complex terrain features in DEM, this study proposes a deep residual frequency-adaptive DEM super-resolution reconstruction model. The model consists of multiple high and low-frequency feature extraction modules forming a residual network structure, enhancing the overall perception of DEM features. Additionally, a frequency selection feature extraction module is integrated to improve the identification and capture of complex terrain features. The model also incorporates atrous spatial pyramid pooling, which merges multi-scale information to enhance reconstruction quality and retain detailed terrain features and structures. Final super-resolution reconstruction is completed under dual constraints in the gradient and height domains. Experimental results demonstrate that using elevation maps of the Qinling Mountains in Shaanxi with two different accuracies as test data, the deep residual frequency-adaptive DEM super-resolution model outperforms other advanced models across various metrics. Reconstructed DEMs exhibit richer details and clearer textures.