“互联网+旅游”背景下旅游网络关注度为了解景区旅游需求时空变化提供了重要的切入点。基于百度指数数据分享平台，通过获取2011~2020年北京市68家高A旅游景区的网络搜索数据，借助Python和ArcGIS软件，运用年际变动指数、季节性强度指数和地理集中指数对其进行时间和空间上的特征分析。研究发现：1) 北京市5A高等级景区和1A初等级景区数量较少，“中间大，两头小”的纺锤体结构仍有优化改进的空间。2) 旅游景区网络关注度呈现出旺季较长、平季和淡季较短的特征，并且月度特征由“双峰”向“多峰”转变，高峰出现在4月、8月和10月，与实际旅游流基本吻合。3) 节假日期间网络关注度呈现先升后降趋势，并且随着年份由倒“U”型转变为倒“V”型，其中“五一”关注度高峰出现在4月29~30日，具有明显的前兆效应，“十一”关注度高峰出现在10月2~3日，与实际旅游流趋于一致。4) 全国31个省市对北京高A景区关注度存在明显差异，表现为“近高远低、东高西低”的空间分布特征，主要是地理空间距离和社会经济发展水平综合影响的结果。 The tourism network concern degree under the background of “Internet +Tourism” provides an important breakthrough point for understanding the temporal and spatial changes of tourist demand in scenic spots. Based on the Baidu index data sharing platform, this paper obtains the online search data of 68 High-grade scenic spots in Beijing from 2011 to 2020, and uses Python and ArcGIS software to analyze their temporal and spatial characteristics by using the interannual variation index, seasonal intensity index and geographic concentration index. The results show that: 1) The number of 5A scenic spots and 1A scenic spots in Beijing is small, and the spindle structure of “big in the middle, small at both ends” still has room for optimization and improvement. 2) The network attention of scenic spots shows the characteristics of longer peak season, shorter average season and off-season, and the monthly characteristics change from “double peak” to “multi peak”. The peak appears in April, August and October, which is basically consistent with the actual tourism flow. 3) During the holidays, the network attention shows a trend of first rising and then falling, and changes from inverted “U” type to inverted “V” type with the year. The peak of attention on the “May Day” appears from April 29 to 30, which has obvious precursor effect. The peak of attention on the “National Day” appears from October 2 to 3, which is consistent with the actual tourism flow. 4) There are obvious differences in the attention of 31 provinces and cities to Beijing high-grade scenic spots, which shows the spatial distribution characteristics of “near high and far low, east high and west low”, mainly due to the comprehensive influence of geographical spatial distance and the level of social and economic development.