【摘要】 为对区块链技术处理的数据进行更加清晰和直观的展示，基于特征提取和机器学习的方法进行区块链数据可视化模型的构建。其中，以k最近邻算法KNN作为可视化的实现算法，并构建SRR-Voting预测模型进行数据的预测，最终将预测数据通过D3.js绘图模块绘制成图，完成可视化展示。实验结果表明，设计的基于特征提取和机器学习的数据可视化模型能够进行清晰直观的数据可视化展示，信息准确且完整；与其他数据预测模型相比，SRR-Voting预测模型具有更好的预测效果，F,Recall和Precision分别达到了0.975,0.976,0.975,精度较高。以上结果表明，设计的基于特征提取和机器学习的数据可视化模型能够进行效果良好的数据可视化展示，能够应用于实际的场景进行可视化设计，可靠性较高。更多还原

【Abstract】 In order to display the data processed by the blockchain technology more clearly and intuitively, the blockchain Data and information visualization model is built based on the methods of feature extraction and machine learning. Among them, the k-nearest neighbor algorithm KNN is used as the visualization implementation algorithm, and an SRR-Voting prediction model is constructed for data prediction. Finally, the predicted data is drawn into a graph through the D3.js drawing module to complete the visualization display. The experimental results show that the designed Data and information visualization model based on feature extraction and machine learning can display Data and information visualization clearly and intuitively, and the information is accurate and complete; Compared with other data prediction models, the SRR-Voting prediction model has better prediction performance, with F, Recall, and Precision reaching 0.975, 0.976, and 0.975, respectively, with higher accuracy. The above results show that the designed Data and information visualization model based on feature extraction and machine learning can display Data and information visualization with good results, and can be applied to the actual scene for visual design with high reliability.更多还原