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基于深度学习的马克思主义哲学与系统论的关联环境分析。

Analysis of the Relevance Environment between Marxist Philosophy and System Theory Based on Deep Learning.

机构信息

Mudanjiang Normal University, Mudanjiang 157011, China.

出版信息

J Environ Public Health. 2022 Jul 31;2022:6322272. doi: 10.1155/2022/6322272. eCollection 2022.

Abstract

In social science and natural science, MP (Marxist Philosophy) has played an active role in promoting its development, and MP also guides people's practice and understanding. There is an inevitable connection with system theory MP. In a sense, both system theory and PMbelong to methodology and both contain the viewpoints of movement and development. In this paper, various text features in natural scenes are discussed in detail, and the original vector is studied by using CNN (Convective Neural Network) of DL (Deep Learning), so as to construct a one-dimensional text vector and realize the mutual influence and continuous optimization of feature extraction and text clustering. The experimental results show that under the condition of calculating the current cosine similarity measure, the accuracy rate is the highest, reaching 93.67%. This algorithm can effectively improve its performance in text classification tasks on large data sets.

摘要

在社会科学和自然科学中,马克思主义哲学(MP)在促进其发展方面发挥了积极作用,同时也指导着人们的实践和认识。它与系统理论(MP)之间存在着必然的联系。从某种意义上说,系统理论和 PM 都属于方法论,都包含着运动和发展的观点。本文详细讨论了自然场景中的各种文本特征,并通过深度学习(DL)中的卷积神经网络(CNN)对原始向量进行研究,从而构建一维文本向量,并实现特征提取和文本聚类的相互影响和连续优化。实验结果表明,在计算当前余弦相似性度量的条件下,准确率最高,达到 93.67%。该算法可以有效地提高其在大数据集上的文本分类任务中的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a3d/9357689/3bf1e7f988bb/JEPH2022-6322272.001.jpg

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