Deep learning is the latest trend of machine learning and artificial intelligence research. As a new field with rapid development over the past decade, it has attracted more and more researchers’ attention. Convolutional Neural Network (CNN) model is one of the most important classical structures in deep learning models, and its performance has been gradually improved in deep learning tasks in recent years. Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn the feature representation of sample data. Firstly, this paper analyzes the model structure of a typical convolutional neural network model to increase the network depth and width in order to improve its performance, analyzes the network structure that further improves the model performance by using the attention mechanism, and then summarizes and analyzes the current special model structure. In order to further improve the text language processing effect, a convolutional neural network model, Hybrid convolutional neural network (CNN), and Long Short-Term Memory (LSTM) based on the fusion of text features and language knowledge are proposed. The text features and language knowledge are integrated into the language processing model, and the accuracy of the text language processing model is improved by parameter optimization. Experimental results on data sets show that the accuracy of the proposed model reaches 93.0%, which is better than the reference model in the literature.

中文翻译：

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基于Hybrid CNN和LSTM的语言处理模型构建与仿真

深度学习是机器学习和人工智能研究的最新趋势。作为近十年来快速发展的新兴领域，它引起了越来越多研究者的关注。卷积神经网络（CNN）模型是深度学习模型中最重要的经典结构之一，近年来其性能在深度学习任务中逐渐得到提升。卷积神经网络因其能够自动学习样本数据的特征表示而被广泛应用于图像分类、目标检测、语义分割和自然语言处理等领域。首先分析了典型的卷积神经网络模型的模型结构，增加网络深度和宽度以提高其性能，分析了利用注意力机制进一步提高模型性能的网络结构，然后总结分析当前的特殊模型结构。为了进一步提高文本语言处理效果，提出了基于文本特征和语言知识融合的卷积神经网络模型、混合卷积神经网络（CNN）和长短期记忆网络（LSTM）。将文本特征和语言知识融入到语言处理模型中，通过参数优化提高文本语言处理模型的准确性。数据集上的实验结果表明，所提模型的准确率达到93.0%，优于文献中的参考模型。