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乡村振兴战略在推动城乡融合发展、缩小城乡差距方面扮演着关键角色。本文从产业兴旺、生态宜居、生活富裕、乡风文明和治理有效四个维度构建甘肃省县域乡村振兴评价指标体系，利用熵值-TOPSIS法，测算甘肃省地区（州）86个县（区）2013—2019 年乡村振兴综合水平以及各子系统水平，并利用Dagum基尼系数，测度乡村振兴空间差异水平，采用Kernel密度图、马尔科夫链，分析不同地区乡村振兴水平动态演变趋势。最后，运用空间计量、神经网络分析甘肃省乡村振兴水平的影响因素以及乡村振兴区域内、外部水平差异未来趋势，研究发现: （1）甘肃省整体乡村振兴水平从2013年的0.1378上升到2019年的0.1780，增长幅度约为29.17%，表明甘肃省县域乡村振兴总体水平并不高，但增幅较快。其中，乡风文明与治理有效得分相对最高，其均值为0.1802，生态宜居得分最低，为0.1113；说明在农村生态宜居方面甘肃省整体较弱，水平较低。各市州乡村振兴水平区域差异显著，均值差异尤为突出，张掖、酒泉、嘉峪关、金昌等地区的乡村振兴发展水平位居前列，而定西、甘南、临夏等地区则相对落后。（2）乡村振兴整体发展水平基尼系数整体呈下降态势，表明当前甘肃省乡村振兴发展水平总体差异在逐步缩小；基尼系数由2013年的0.20561降至2019年的0.18612，降幅为9.48％，说明甘肃省乡村振兴水平的总体差异并不十分突出。河西、甘南、临夏地区基尼系数呈现波动下降趋势，陇东南地区呈波动上升趋势，陇中地区在2019年却有所上升，且区域内差异数值多位于0.15左右，说明内部差距相对较大；对于区域间差异，陇东南-甘南临夏地区降幅最大，陇中-陇东南地区基尼系数有轻微波动趋势。就总体差异分解贡献度来看，区域间差异贡献率最大，为47.33%。其次为区域间超变密度，为30.34%，区域内差异贡献度最小，为22.32%。（3）根据动态演进趋势，甘肃省县域乡村振兴整体水平呈逐年递增态势，且增速较快，并未出现突出极化现象，县域间乡村振兴水平差距也逐年缩小。然而，河西地区存在极化现象，且绝对差异有轻微扩大趋势。陇中地区绝对差异逐年缩小，没有出现极化现象，且整体水平逐年上升。陇东南地区乡村振兴水平逐年递增，绝对差异呈递减态势，但存在极化现象。甘南、临夏地区乡村振兴水平初始年份伴有极化现象，且极化现象近几年有所减弱，但该地区整体水平偏低，且差异略微有所扩大。从动态转移矩阵来看，甘肃省乡村振兴水平多处于“Ⅰ、Ⅱ”两种相对低水平状态，具有大概率保持原有不变和向下转移的趋势，且存在“俱乐部”收敛现象。（4）从影响因素来看，人均收入水平的提升对于河西、陇东南地区产生积极的带动作用，农村财政支出、金融发展水平对临夏、甘南地区表现出了积极的带动作用。其中，农村财政支出空间正向溢出效应极为显著，而金融发展水平以及产业结构的空间溢出效应却不突出。从预测分析来看，人均收入水平提升对降低区域内部差异具有显著影响，农村固定资产投资以及农村财政支出提升将有效降低区域内部差异，产业结构升级会显著降低区域间差距，而人均收入水平、金融发展水平、农村财政支出的增加对区域整体差距改善则较为明显。

The rural revitalization strategy plays a key role in promoting the integrated development of urban and rural areas and narrowing the gap between urban and rural areas.The rural revitalization strategy plays a key role in promoting the integrated development of urban and rural areas and narrowing the gap between urban and rural areas. This paper constructs the evaluation index system of the level of rural revitalization at the county level from four dimensions: industrial prosperity, ecological livability, awealth of life, rural civilization and effective governance.The Entropy-TOPSIS method is used to measure the level and comprehensive level of each subsystem of rural revitalization in 86 counties (districts) in Gansu Province from 2013 to 2019,and use Dagum Gini-Index to investigate the spatial difference level of rural revitalization, and use Kernel density map and Markov chain to analyze the dynamic evolution trend of rural revitalization level in different regions.Finally, using spatial measurement and neural network to analyze the influencing factors of the level of rural revitalization in Gansu Province and the future trend of the difference between the internal and external levels of rural revitalization in the region, the study found that:(1)The overall level of rural revitalization in Gansu Province has risen from 0.1378 in 2013 to 0.1780 in 2019, with an increase rate of 29.17%, indicating that the overall level of rural revitalization in counties in Gansu Province is not high, but the growth rate is fast.Among them, the score of rural civilization and governance effectiveness is relatively the highest, with an average of 0.1802, and the score of ecological livability is the lowest, with 0.1113,It shows that Gansu Province is weak and low in terms of rural ecological livability. The regional difference of rural revitalization level among cities and prefectures is significant, especially the average difference. Zhangye, Jiuquan, Jiayuguan, Jinchang and other regions have the highest level of rural revitalization and development, while Dingxi, Gannan, Linxia and other regions are relatively backward.(2)The Gini-Index of the overall development level of rural revitalization has declined, indicating that the overall difference in the current level of rural revitalization in Gansu Province is gradually narrowing; The Gini-Index decreased from 0.20561 in 2013 to 0.18612 in 2019, with a decrease of 9.48%, indicating that the overall difference in the level of rural revitalization and development in Gansu Province is not very prominent.The Gini-Index in Hexi, Gannan and Linxia regions showed a fluctuating downward trend, while that in southeastern Gansu showed a fluctuating upward trend, while that in central Gansu increased in 2019, and the regional difference value was mostly around 0.15, indicating that the internal gap was relatively large.For the regional differences, the decline in Linxia area from southeast Gansu to southern Gansu is the largest, and the Gini-Index in the region from central Gansu to southeast Gansu has a slight fluctuation trend. From the perspective of overall difference contribution, the contribution rate of regional difference is the largest, 47.33%. The inter-regional difference is 22.32%, and the inter-regional hyper-variable density is 30.34%.(3)The Gini-Index of the overall development level of rural revitalization in Gansu Province shows a downward trend, indicating that the overall difference of the development level of rural revitalization in Gansu Province is gradually narrowing.The Gini-Index decreased by 9.48% from 0.20561 in 2013 to 0.18612 in 2019, indicating that the overall difference in the level of rural revitalization in Gansu Province is not very significant.The Gini-Index in Hexi, Gannan and Linxia showed a decreasing trend, the Gini-Index in southeastern Gansu showed an increasing trend, and the Gini coefficient in central Longong increased in 2019, and the inter-regional difference values were mostly about 0.15, indicating a relatively large internal gap.In terms of regional differences, the Gini-Index of Longzhong-Longdongnan region showed a slight fluctuation trend, while the decline of the Gini-Index of Longdongnan region was the largest.In terms of the contribution degree of overall difference decomposition, the largest contribution rate is the contribution rate of regional difference, which is 47.33%. Inter-regional super-variable density (30.34%) followed, and inter-regional difference contributed the least (22.32%).(4) From the perspective of influencing factors, the improvement of per capital income has a positive driving effect on Hexi and southeast Gansu, and the level of rural fiscal expenditure and financial development has a positive driving effect on Linxia and Gannan. Among them, the spatial positive spillover effect of rural fiscal expenditure is very significant, but the spatial spillover effect of financial development level and industrial structure is not prominent.From the forecast analysis, the improvement of per capital income level has a significant impact on reducing the inter-regional differences, the improvement of rural fixed asset investment and rural fiscal expenditure will effectively reduce the inter-regional differences, the upgrading of industrial structure will significantly reduce the inter-regional gap, and the increase of per capital income level, financial development level and rural fiscal expenditure will significantly improve the overall regional gap.