Event detection is a key task in information extraction that aims to identify event trigger words from text and classify them into event types. Existing methods focus more on the trigger word itself, overlooking the dependencies between contexts and lacking comprehensive feature extraction. Therefore, this paper proposes the model TBEF (TAP-BERT + Event Fusion Layer + CRF). First, the TAP-BERT pretrained model for word vector embedding—which incorporates word embedding matrix decomposition, feed-forward neural network pruning, and self-attention mechanism pooling in BERT—is utilized to obtain richer contextual semantic information. Then an event fusion layer consisting of attention and gate mechanisms is employed to better compute the correlation between events and trigger words. Finally, the sequences are labeled by Conditional Random Field (CRF) integrating BIO labeling. Experimental results show that the proposed method yields an F₁ value of 68.55% on the MAVEN dataset, achieving advanced performance for event detection.