创新在全国现代化建设全局中占据核心地位，城市是创新活动开展的主体区域，科学测度城市创新能力对于提升城市竞争力及制定创新战略具有重要价值。以黄河流经区域中的48个城市作为研究对象，从人才培养能力、科技研发能力、经济支撑能力和环境服务能力4个维度构建了城市创新评价指标体系，运用熵权法、Jenks Natural Breaks分类法、基尼系数、Moran’s I等数据分析方法，测度了黄河流域城市创新能力，分析了黄河流域城市创新水平的空间分异及主要障碍因素。结果表明：（1） 黄河流域整体创新能力不高，少数高值城市与其余城市之间得分差距显著，创新能力呈现上、中、下游阶梯式递增趋势。（2） 以朔州—陇南为线，城市创新能力呈现出东南高西北低的分布状态，且各维度的空间分布与总创新能力趋于一致。（3） 黄河流域城市创新能力分布处于较不均衡状态，空间集聚特征表现为正向的空间关联，主要属于低值集聚的空间模式。（4） 科技研发能力与人才培养能力对黄河流域城市创新能力的提高影响程度较大，其中有效发明数是各城市共同的障碍指标。

Innovation occupies a central position in the overall situation of national modernization, and cities are the main areas where innovation activities are performed. Thus, scientific measurement of urban innovation capacity is critical for improving the competitiveness of cities and formulating innovation strategies. Taking 48 cities in the Yellow River Basin as research objects, we constructed a city innovation evaluation index system based on four dimensions: talent cultivation capacity, scientific and technological research and development capacity, economic support capacity, and environmental service capacity. We measured the innovation capacity of these cities using the entropy weight method, Jenks natural breaks classification method, Gini coefficient, Moran’s I index, and other data analysis methods. The spatial differentiation of the innovation level of these cities and the main obstacle factors were analyzed. The results are as follows: (1) The overall innovation capacity of the Yellow River Basin is not high, the score gap between a few high-value cities and the rest of the cities is significant, and the innovation capacity shows the trend of stepwise increment in the upper, middle, and lower reaches. (2) Taking Shuozhou-Longnan as a line, the urban innovation capacity shows a distribution of high in the southeast and low in the northwest, and the spatial distribution of each dimension tends to be consistent with the total innovation capacity. (3) The distribution of urban innovation capacity in the Yellow River Basin is in an unbalanced state, and the spatial agglomeration characteristics show positive spatial correlations, mainly belonging to the spatial pattern of low-value agglomeration. (4) The scientific and technological research and development capacity and talent cultivation capacity have a greater degree of influence on the improvement of the innovation capacity of the cities, where the number of effective inventions is a common obstacle indicator for all cities.