With the rapid proliferation of wireless devices, ensuring secure communication over open wireless channels has become increasingly critical. Physical layer key generation has emerged as a lightweight and information-theoretically secure cryptographic mechanism, offering a promising complement to traditional cryptographic approaches. However, in Internet of Things (IoT) scenarios characterized by heterogeneous devices and highly dynamic environments, conventional physical-layer key generation methods suffer from low key generation rates, poor stability, and limited adaptability. To address these challenges, this paper proposes a novel AI-based physical layer key generation framework that integrates multi-source signal fusion and intelligent feature selection. Specifically, deep learning models are employed to fuse features from multiple heterogeneous wireless signal sources, enabling the extraction of high-quality randomness and enhancing key entropy and security. In parallel, an attention mechanism is employed to dynamically select the most suitable physical layer features based on real-time environmental conditions, thereby enhancing the system’s adaptability and robustness in complex IoT settings. Finally, we outline potential future research directions and discuss the feasibility of implementing the proposed framework in real-world deployments.