基于机器学习的网络感知评估方法

提升网络感知和客户满意度一直是网络优化的工作主线，而KPI指标无法反映网络真实感知情况，传统通过调研了解客户满意度的方式存在很大局限性。本文深入研究了KPI指标和网络真实感知的映射关系，通过大数据挖掘和机器学习建模实现了感知权重因子的量化，以此为基础完成了一种基于机器学习的网络感知评估方法，为客户满意度提升工作提供了全新的分析思路和支撑手段。

Network Quality of Experience Evaluation Based on Machine Learning

Improving network Quality of Experience(QoE) and customer satisfaction has always been the primary target of network optimization and maintenance, while KPI index cannot reflect the real network QoE, and the traditional way of analyzing customer satisfaction through feedback survey has great limitations. In this paper, the mapping relationship between KPI index and network QoE is deeply studied, and the perception weight factor is quantified through big data mining and machine learning modeling. Based on this, a network QoE method based on KPI index is completed, which provides a new way for customer satisfaction analyzing and improvement