In this paper, we propose a document-level event argument extraction model called BSDM based on bidirectional span detection, mainly to address the problems of insufficient use of known information and overlooked correlations between span boundaries in document-level event argument extraction tasks. Firstly, in order to fully utilize known information, we design event types and event roles as event templates in the data preprocessing stage. Then, we integrate trigger and event type information into event roles in the information fusion layer, and identify argument boundaries under the guidance of role information. Secondly, in order to establish correlations between span boundaries, we design forward/backward decoders for the model, decoding argument spans from starting/ending boundaries respectively. Instead of using independent span boundary recognition methods, our model uses de-coded argument boundaries to recognize pending argument boundaries. Finally, considering the core role of trigger in events, we regard event detection as an auxiliary task for document-level event argument extraction. Through multitask joint training, the model will have better performance. We conducted sufficient experiments on the RAMS dataset. The results show that when using BERT-base as the encoder, BSDM achieved an F1 score 0.7% higher than the current SOTA result, and when using BERT-large as the encoder, it showed performance only second to the current SOTA model.