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Event detection is a key task in the field of natural language processing, aiming to identify event trigger words and correctly classify their event types. Sentence-level event detection methods fail to effectively utilize intra-sentence and inter-sentence event relevance information, facing numerous challenges such as polysemy and event co-occurrence. Additionally, neural network-based event detection models require a large amount of text data for training, but the scarcity of corpus data severely affects the accuracy of results and the stability of the model. To address these issues, this paper proposes a document-level event detection method based on information aggregation and data augmentation, called LGIA (local and global information aggregation). This method adopts an encoder-decoder framework, designing a sentence-level local information extraction module based on dilated convolutional networks and a document-level global information extraction module based on conditional layer normalization, to deeply explore the contextual semantic information and the event correlations of the entire document. Meanwhile, this paper employs a data augmentation strategy of synonym replacement to effectively expand the data samples, thereby alleviating the impact of data scarcity. Experimental results validate that the proposed LGIA method achieves good results on the ACE2005 dataset and significantly improves performance on the augmented TAC-KBP2017 dataset, with F1 scores reaching 77.6% and 65.3%, respectively, demonstrating superior performance compared with existing baseline methods.