The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.

中文翻译：

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一种基于注意力引导深度神经网络的新型股指智能预测算法

股市受经济市场、政策等多种因素的影响，其内部变化规律极其复杂。随着股市的快速发展和投资者规模的扩大，股市产生了大量的交易数据，使得获取有价值的信息变得更加困难。由于深度神经网络擅长处理数据量大、非线性映射关系复杂的预测问题，本文提出了一种注意力引导的深度神经网络股票预测算法。本文综合每日股票社交媒体文本情感指数和股票技术指数作为数据源，将其应用于长短期记忆神经网络（LSTM）模型对股市进行预测。基于注意力机制和手套词向量表示算法构建双向长短期记忆神经网络（Bi-LSTM）的社交文本分类情感模型，提取股票情感指数。此外，构建了基于决策树（DT）和主成分分析（PCA）的降维模型，对股票技术指标进行降维，提取主要数据信息。此外，本文提出了一种基于 n​​asNet 的模式识别模型。识别结果可用于自动识别短期K线形态，预测可靠交易信号，帮助投资者定制短期高效投资策略。实验结果表明，所提算法的预测准确率可达98.6%，