Interior design is the art and science of upgrading interior spaces to create a practical and aesthetically pleasant environment. It entails envisioning, planning and implementing designs that take into consideration spatial layout, color schemes, vases and pottery choices, lighting, along with decorative components, while balancing functionality with the client’s preferences and the space’s purpose. The purpose of this research is to develop an innovative VR technology integrated interior design method for living space experience. We propose a novel sea horse optimized-versatile deep convolutional neural network (SHO-VDCNN) algorithm for recognizing innovative interior designs in living spaces. At first we gathered a dataset, to train our proposed recognition model. Image standardization is employed to preprocess the gathered data. In this research, we utilize a VR interior design layout mechanism to improve the potential of autonomous layout interior while enhancing interactions of machines with virtualization. Furthermore, the optimal placements (states) for these internal model components in simulated environments could be spontaneously identified by employing the deep Q-learning network (DQN) method. The proposed model is implemented in Python software. We assessed our suggested framework with various evaluation metrics. We also conducted a comparison analysis to examine the effectiveness of the suggested paradigm. The experimental findings demonstrate that the approached recognition model performed better than other traditional learning models for recognizing interior design frameworks in living spaces.