Using hierarchical CNN, the company's multiple news is characterized as three levels: sentence vectors, chapter vectors, and enterprise sentiment vectors. By combining the stock price data with the news lyric data at the same time, the influence of news on price is used to achieve correlation analysis of news information and stock prices. A two-channel attention mechanism fusion model based on CNN-LSTM is proposed. After the dual-channel feature extraction, the attention layer fusion layer is used to convert the weighted values of LSTM hidden variables, so the stock price can be predicted with the news text.

中文翻译：

---

基于CNN-LSTM的股价预测双通道注意力机制融合模型

使用分层CNN，该公司的多个新闻被表征为三个层次：句子向量、章节向量和企业情感向量。通过将股价数据与新闻歌词数据同时结合，利用新闻对价格的影响，实现新闻信息与股价的相关性分析。提出了一种基于CNN-LSTM的双通道注意力机制融合模型。双通道特征提取后，注意力层融合层用于转换LSTM隐藏变量的加权值，从而可以用新闻文本预测股票价格。