In intelligent networks, multiple service flows have different transmission requirements in terms of delay and bandwidth, and the burstiness of self-similar traffic exacerbates delay and packet loss rate. To address this problem, an improved WFQ scheduling algorithm based on traffic prediction (LPR-WFQ) is proposed. This algorithm uses the TLGP strategy to classify traffic based on the mean and variance of traffic. Based on the Bayesian estimation idea, it predicts future traffic levels by calculating conditional transition probabilities. The weights are dynamically adjusted based on the prediction results and the mean arrival rate, thereby reducing delay and packet loss, improving service quality, and optimizing the calculation method of virtual finish time. Simulation results show that compared with other scheduling algorithms, this algorithm improves the delay, delay jitter, throughput and packet loss by 6.01%, 9.66%, 5.37% and 38.57% respectively, indicating that the algorithm can meet the performance requirements of differentiated services.