This paper addresses the challenge of spectral efficiency (SE) reduction in reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems due to the heavy pilot overhead required for sensing capabilities. To tackle this issue, the authors propose a superimposed symbol scheme that integrates sensing pilots with data symbols over the same time-frequency resources. They develop a structure-aware sparse Bayesian learning (SBL) framework where decoded data symbols serve as side information to enhance sensing performance and increase SE. Additionally, they propose a low-complexity algorithm for simultaneous communication and localization of multiple users, employing unitary approximate message passing (UAMP) for initial angle estimation and iterative refinements through reduced-dimension matrix calculations. The sparse code multiple access (SCMA) technology is also incorporated into the iterative framework to suppress interference and facilitate accurate data detection and localization. Numerical results demonstrate that the proposed scheme achieves centimeter-level localization while achieving up to 96% of the SE of conventional communications without sensing capabilities, and provides an effective throughput improvement over 133% compared to other typical ISAC schemes.This paper addresses the challenge of spectral efficiency (SE) reduction in reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems due to the heavy pilot overhead required for sensing capabilities. To tackle this issue, the authors propose a superimposed symbol scheme that integrates sensing pilots with data symbols over the same time-frequency resources. They develop a structure-aware sparse Bayesian learning (SBL) framework where decoded data symbols serve as side information to enhance sensing performance and increase SE. Additionally, they propose a low-complexity algorithm for simultaneous communication and localization of multiple users, employing unitary approximate message passing (UAMP) for initial angle estimation and iterative refinements through reduced-dimension matrix calculations. The sparse code multiple access (SCMA) technology is also incorporated into the iterative framework to suppress interference and facilitate accurate data detection and localization. Numerical results demonstrate that the proposed scheme achieves centimeter-level localization while achieving up to 96% of the SE of conventional communications without sensing capabilities, and provides an effective throughput improvement over 133% compared to other typical ISAC schemes.