Federated learning (FL) systems enable collaborative model training among industrial Internet of Things (IIoT) devices but face significant security challenges, particularly in backdoor attacks, due to the nonindependent and identically distributed (non-IID) nature of data. To address this challenge, we propose a stability-enhanced dynamic backdoor defense approach in FL for IIoT, which maintains primary task accuracy while strengthening defenses in non-IID environments. Leveraging the similarity between data distribution and model updates, we segment non-IID scenarios into multiple quasi-IID environments. Our approach includes a dynamic client matching module, a malicious filtering module, and robust personalized aggregation to reduce the success rate of backdoor attacks while augmenting the resilience and precision of the aggregated model. The effectiveness of our strategy has been validated through analyses on the Modified National Institute of Standards and Technology database (MNIST), Canadian Institute for Advanced Research, 10 classes (CIFAR-10), Internet of Things (IoT)-23, and Washington University in St. Louis (WUSTL)-IIOT datasets in both IID and non-IID scenarios. Notably, on the IoT-23 and WUSTL-IIOT, the success rate of backdoor attacks was significantly reduced to 3.46%.