Choosing an appropriate prior distribution for the weights of Bayesian neural networks (BNNs) remains an open challenge. In most cases, a Gaussian prior is adopted, but its typically high variance can lead to overestimation of the predictive uncertainty. Recently, horseshoe priors have been proposed for model selection and compression, as they effectively deactivate units that do not contribute to explaining the data and yield well-calibrated structural weight uncertainty estimates. However, the horseshoe prior has been found to underestimate predictive uncertainty, especially in regions lacking data. In this paper, we proposed an efficient variational sparse BNN that integrates both a regularized horseshoe prior and a Gaussian scale mixture prior. Both priors can induce sparsity, thereby mitigating overfitting and improving the model's generalization ability. Our approach enables computationally efficient optimization via variational inference while providing more reliable predictive uncertainty. Experimental results demonstrate that the proposed model delivers competitive predictive performance and reasonable posterior weight uncertainty estimates in non-linear regression, image classification, and anomaly detection tasks compared with recent methods.