The complex structure and diverse imformation of stock markets make stock movement prediction extremely challenging. However， most of the existing studies treat each stock as an individual or use graph structures to model complex higher-order relationships in stock markets， without considering the hierarchy and dynamics among stocks， industries and markets. Aiming at the above problems， a Dynamic Macro Memory Network （DMMN） was proposed， and price movement prediction was performed for multiple stocks simultaneously based on DMMN. In this method， the market macro-environmental information was modeled by the hierarchies of “stock-industry-market”， and long-term dependences of this information on time series were captured. Then， the market macro-environmental information was integrated with stock micro-characteristic information dynamically to enhance the ability of each stock to perceive the overall state of the market and capture the interdependences among stocks， industries， and markets indirectly. Experimental results on the collected CSI300 dataset show that compared with stock prediction methods based on Attentive Long Short-Term Memory （ALSTM） network， GCN-LSTM （Graph Convolutional Network with Long Short-Term Memory）， Convolutional Neural Network （CNN） and other models， the DMMN-based method achieves better results in F1-score and Sharpe ratio， which are improved by 4.87% and 31.90% respectively compared with ALSTM， the best model among all comparison methods. This indicates that DMMN has better prediction performance and better practicability.

Event Detection （ED） is one of the most important tasks in the field of information extraction， aiming to identify instances of specific event types in text. Existing ED methods usually use adjacency matrix to express syntactic dependencies， however， the adjacency matrix often needs to be encoded with Graph Convolutional Network （GCN） to obtain syntactic information， which increases the complexity of the model. Therefore， an event detection method without trigger words incorporating syntactic information was proposed. After converting the dependent parent word and its context into a position marker vector， the word embedding of dependent sub-word was incorporated at the source end of the model in a parameter-free manner to strengthen the semantic representation of the context， without the need of GCN for encoding. In addition， for the time-consuming and laborious labeling of trigger words， a type perceptron based on the multi-head attention mechanism was designed， which was able to model the potential trigger words in the sentence to complete the event detection without trigger words. In order to verify the performance of the proposed method， experiments were conducted on the ACE2005 dataset and the low-resource Vietnamese dataset. Compared with the Event Detection Using Graph Transformer Network （GTN-ED） method， the F1-score of the proposed method was increased by 3.7% on the ACE2005 dataset； compared with the binary classification method Type-aware Bias Neural Network with Attention Mechanisms （TBNNAM）， the F1-score of the proposed method was increased by 9% on the Vietnamese dataset. The results show that the integration of syntactic information into Transformer can effectively connect the scattered event information in the sentence to improve the accuracy of event detection.