Traffic congestion on urban roads has seriously affected people’s daily life and work as well as the sustainable development of the city. To accurately and timely identify the traffic congestion status of urban roads, a fuzzy C-means clustering algorithm based on kernel and weighting (WKFCM) for identifying the traffic congestion status of urban roads is proposed. For the collected traffic data information, different identification and processing methods are adopted to handle redundant data, missing data, and abnormal parameters of erroneous data, obtaining effective and complete data. On this basis, the WKFCM algorithm model is constructed, and the results are evaluated by using the cross-estimation method of misjudgment rate, and the validity of clustering is judged according to the number of misjudgment samples. The experimental results show that according to the traffic data of flow, density, and speed, the identification results are almost consistent with the actual results. The model demonstrates a high data identification rate and an impressive average identification performance, enabling it to accurately and in real-time identify urban road traffic congestion status.