Entity and relation extraction from text without a fixed format is a vital step in knowledge graph construction. Pipeline methods overlook the interaction between two sub-tasks and are susceptible to error propagation issues. To tackle this issue, we propose a bidirectional entity relation joint extraction framework based on multi-layer perceptron (MLPBRE). This frame-work employs bidirectional extraction methods (S2T and O2T) to extract relations starting from both head and tail entities, and it enhancing feature representation with multi-layer perceptron. Experimental results of the model show that MLPBRE achieves F1 scores of 92.7% and 92.8% on the NYT and NYT datasets, and 93.2% and 89.7% on the WebNLG* and WebNLG datasets, respectively, surpassing current methods. The method particularly excels in handling sentences with overlapping triplet issues and those containing multiple triplets. The proposed approach significantly reduces error propagation issues between two sub-tasks and it enhances the overall performance of entity relation joint extraction tasks.