In order to address the security problem arising from the numerous devices and limited terminal resources in the internet of things (IoT) environment, a method for radio frequency (RF) fingerprint signal recognition of IoT devices based on lightweight omni-dimensional dynamic convolutional neural network(LR-ODCNN) is proposed. Firstly, the LR-ODCNN model is designed. Then, the baseband signals of the devices are collected using an optical transmission system, and the I and Q signals are extracted from the baseband signals as the input to the network. Finally, the LR-ODCNN model adapts to the signal characteristics of different devices based on a multi-dimensional attention mechanism and performs signal feature extraction and recognition. The experimental results show that the average recognition accuracy of the LR-ODCNN model is 94.35% at transmission distances of 10 m, 400 m, 1.7 km, and 8.6 km., which is an improvement of 5.35% and 10.13% compared to the McAFF model and the Oracle model respectively. Additionally, it boasts strong robustness and lightweight.