The advance in network technology and the rise of multi-access edge computing have led to the deployment of computation and network resources closer to the end users. As the service numbers increase, it is a challenge to predict the quality of service (QoS) in real-time and accurately in the complex and dynamic edge computing environment to better recommend services to users. In this study, a deep neural model for real-time QoS prediction based on service load (QPSL) is proposed, which can provide missing load condition awareness and cycle awareness for QoS prediction in edge computing. Firstly, the service load condition is characterized, and the features of the time-series are obtained by the time-series decomposition module. Secondly, CNN and BiLSTM are combined to learn the potential time-series relationships and generate the state vectors at different time intervals. Then, the state vectors at future time intervals are constructed by assigning weights to the historical state vectors based on the Attention mechanism. Finally, contextual embedding vectors and state vectors are fed into the perception layer to complete the real-time QoS prediction. Extensive experiments are conducted based on a real fusion dataset, and the results show that QPSL improves MAE by 10.28% and 10.87% on average for response time and throughput tasks respectively, outperforming existing time-aware QoS prediction methods.