The simultaneous localization and recognition of subwavelength non-cooperative entities within complex multi-scattering environments using a simplified system continues to pose a substantial challenge. In this paper, we address this challenge by synergistically integrating time reversal time-frequency phase prints (TRTFPPs) and neural networks. Initially, a time reversal (TR) single-input single-output (SISO) framework is employed to generate TRTFPPs. Subsequently, neural networks are employed to comprehend the TRTFPPs. Specifically, a cascaded neural network structure is embraced, encompassing both a recognition neural network and distinct neural networks for localizing different entities. Using the devised method, we have achieved the simultaneous subwavelength recognition and localization through numerical simulations for metallic entities and experimental testing for metallic and non-metallic entities. The proposed methodology holds applicability across various electromagnetic systems, including but not limited to detection, imaging, human-computer interaction, and the Internet of Things (IoT).