Abstract: With the advent of the big data era, information extraction has become a significant research direction in the field of natural language processing. Information extraction involves multiple tasks, including named entity recognition, relation extraction, and event extraction, each typically relying on specialized models to address its specific challenges. This paper proposes a universal information extraction method based on prompt learning (EBP-UIE), integrating the ERNIE pre-trained language model, bi-directional long short-term memory networks (BiLSTM), and pointer networks, aimed at resolving the complexities of information extraction tasks through a unified framework and facilitating cross-task knowledge sharing. The introduction of the ERNIE model enhances deep text understanding and contextual analysis, the application of BiLSTM strengthens the capture of sequential features and the parsing of long-distance dependencies, and the pointer network improves the precise identification of start and end positions of information elements in text. The experimental results show that on named entity recognition, the F1 scores of EBP-UIE on the MSRA and PeopleDaily datasets are respectively 1.83% and 0.62% higher than those of the existing best models; on relation extraction, the F1 score of EBP-UIE on the DuIE dataset exceeded that of the UIE model by 6.84%; And on the event extraction, EBP-UIE outperforms the UIE model by 4.42% and 0.95% in trigger word and argument extraction performance on the DuEE dataset, respectively.