Xi'an Peihua University, Xi'an, Shannxi 710125, China.
Comput Intell Neurosci. 2022 Apr 28;2022:7008093. doi: 10.1155/2022/7008093. eCollection 2022.
This paper adopts knowledge mapping combined with a deep neural network algorithm to conduct in-depth research and analysis on the current situation and development of the industrial economy and designs a visual analysis model of economic development based on knowledge mapping combined with a deep neural network algorithm. Cultivate the concept of coordinated development and legal system of the subject, improve the awareness of network security and integrity self-discipline of the subject, improve the level of network hardware equipment manufacturing, improve the level of network platform construction, build a network security technology prevention system, improve the repair system of network information alienation, set up a specialized agency setting for the coordinated development of network ecology and industrial economy, and increase the capital investment in network infrastructure and network information technology research and development. A framework of breadth and depth recommendation ranking based on a knowledge graph is proposed and implemented. This paper provides a visual analysis method to sort and classify multivariate data. The method first determines users' preferences through their interactive operations, calculates the weights of each attribute according to the users' preference model, then uses the obtained attribute weight sets to sort the whole data set, and finally completes the category classification according to the sorting results and the users' markings on some data. The visual display allows users to intuitively perform data sorting and classification operations and quickly understand the characteristics and category features of the data. The framework achieves modeling and integration of knowledge graph neighborhood information from breadth dimension and depth dimension to realize personalized recommendation sorting and improves the F1 metrics by 8.59%, 14.36%, and 15.22% on the public datasets Amazon-book, Yelp2018, and ILast-FM compared with the previous optimal model.
本文采用知识图谱结合深度神经网络算法，对产业经济的现状和发展进行深入研究和分析，设计了一种基于知识图谱结合深度神经网络算法的经济发展可视化分析模型。培养主体协调发展和法制观念，提高主体网络安全和完整性自律意识，提高网络硬件设备制造水平，提高网络平台建设水平，构建网络安全技术防范体系，完善网络信息异化修复体系，为网络生态与产业经济协调发展设置专门机构设置，增加网络基础设施和网络信息技术研发的资金投入。提出并实现了一种基于知识图的广度和深度推荐排序框架。本文提供了一种可视化分析方法来对多元数据进行排序和分类。该方法首先通过用户的交互操作来确定用户的偏好，根据用户的偏好模型计算每个属性的权重，然后使用获得的属性权重集对整个数据集进行排序，最后根据排序结果和用户对某些数据的标记完成类别分类。可视化显示使用户能够直观地进行数据排序和分类操作，并快速了解数据的特征和类别特征。该框架从广度和深度维度实现了知识图谱邻域信息的建模和集成，实现了个性化推荐排序，并在公共数据集 Amazon-book、Yelp2018 和 ILast-FM 上与之前的最优模型相比，F1 指标分别提高了 8.59%、14.36%和 15.22%。
