基于改进深度学习算法的大数据交互式查询技术研究

Research on big data interactive query technology based on improved deep learning algorithm

传统大数据交互式查询技术忽略了对大数据融合步骤,导致该技术的交互查询效果不理想,且准确度偏低。为此,提出基于改进深度学习算法的大数据交互式查询技术。以教务系统为研究对象,构建教务系统交互大数据的粗糙量化特征分析模型,基于联合自相似性特征解析控制方法实现对教务系统交互大数据信息融合和特征匹配,从而提取教务系统交互大数据的关联匹配特征量。通过目标样本集相似性特征的重构,实现大数据深度学习训练。利用深度学习模型深度融合教务系统交互大数据,实现对大数据的交互式查询。仿真结果表明,该方法教务系统交互大数据查询的查准率较高,且数据训练过程的收敛速率和传输速率较好,有效提高了大数据交互式查询能力。

The traditional big data interactive query technology ignores the big data fusion steps,which leads to the unsatisfactory interactive query effect and low accuracy.Therefore,an interactive big data query technology based on improved deep learning algorithm is proposed.Taking the educational administration system as the research object,the rough quantitative feature analysis model of interactive big data of educational administration system is constructed.Based on the joint self similarity feature analysis control method,the information fusion and feature matching of interactive big data of educational administration system are realized,and the associated matching feature quantity of interactive big data of educational administration system is extracted.Through the reconstruction of the similarity features of the target sample set,the big data deep learning training is realized.The deep learning model is used to deeply integrate the interactive big data of educational administration system to realize the interactive query of big data.The simulation results show that the precision of interactive big data query in educational administration system is high,and the convergence rate and transmission rate of data training process are good,which effectively improves the ability of interactive big data query.