Line losses are an inevitable issue in the process of power distribution and have a direct impact on the economic performance of power companies. Therefore, accurately predicting line losses has become a focal concern for the power sector. Predicting line losses using recurrent neural networks can encounter issues related to forgetting, making accurate predictions challenging for longer time series. To enhance the accuracy of line loss prediction, a line loss prediction model based on the self-attention mechanism is proposed. Initially, a high-dimensional time series is constructed using information such as the overall power consumption, line losses, and time within a recent period. Subsequently, a self-attention neural network model is established. Then, an open-source dataset is utilized for training and testing, with mean squared error as evaluation metrics. Ultimately, experimental results demonstrate that the line loss prediction model based on the self-attention mechanism can effectively predict line losses, particularly demonstrating strong predictive performance for longer line loss sequences when employing self-attention neural networks.