To enhance the denoising performance of event-based sensors, we introduce a clustering-based temporal deep neural network denoising method (CBTDNN). Firstly, to cluster the sensor output data and obtain the respective cluster centers, a combination of density-based spatial clustering of applications with noise (DBSCAN) and Kmeans++ is utilized. Subsequently, long short-term memory (LSTM) is employed to fit and yield optimized cluster centers with temporal information. Lastly, based on the new cluster centers and denoising ratio, a radius threshold is set, and noise points beyond this threshold are removed. The comprehensive denoising metrics F1_score of CBTDNN have achieved 0.893 1, 0.773 5, and 0.921 5 on the traffic sequences dataset, pedestrian detection dataset, and turntable dataset, respectively. And these metrics demonstrate improvements of 49.90%, 33.07%, 19.31%, and 22.97% compared to four contrastive algorithms, namely nearest neighbor (NNb), nearest neighbor with polarity (NNp), Autoencoder, and multilayer perceptron denoising filter (MLPF). These results demonstrate that the proposed method enhances the denoising performance of event-based sensors.