In order to obtain target information from massive data in optical communication networks, optimize network performance and service quality, a deep mining method for optical communication network data based on improved fuzzy clustering is proposed. Using probability and neighborhood classification methods to separate real-time and historical data streams, and obtain a set of real-time and effective data streams. Using point density function to improve fuzzy clustering algorithm, determining the optimal initial clustering center, and then merging clustering points through inter class distance to accelerate iteration speed. Based on the effectiveness function, the number of clustering centers is determined. Calculate the high-order density spectrum of real-time effective data streams accumulated in two-dimensional space within two discrete sampling periods, correct the update trajectory of the data stream, use differential evolution to optimize fuzzy clustering iteration, and achieve deep data mining in optical communication networks. Experimental results have shown that the improved fuzzy clustering algorithm has good data mining performance and can accurately obtain valuable target information from the network.