The microcontroller of wireless sensor network (WSN) nodes used for mechanical vibration monitoring requires intricate edge computing, yet face limitations in hardware resources. Convolutional neural network (CNN), as a high-performance and commonly used deep learning algorithm, can enhance the computational capabilities of edge WSN nodes when run on microcontroller units (MCUs). This paper proposes a hierarchical decomposition method for CNN models without modification, addressing the challenge of running nonlightweight CNN on resource-constrained MCU and enhancing the computational capabilities of mechanical vibration WSN nodes. First, a file structure is designed to decompose and store CNN model parameters. Subsequently, a memory management method is proposed, and the consumption process of random-access memory is derived. Finally, a parameter localization method is introduced to accurately and efficiently retrieve model parameters. Experiments demonstrated that with only 1. 76 KB of RAM and 2. 14 KB of Flash, high-precision edge computing recognition tasks can be accomplished within 3. 15 ms.