| Enhancing the competitiveness of the cultural tourism industry is fundamental to realizing the transformation and development of tourism and the prosperity of cultural development.In the context of the 14 th five-year plan,improving the competitiveness of the cultural and tourism industry plays an important role in enhancing the comprehensive competitiveness of China’s foreign trade,which has also become the key to building a domestic and foreign "double-cycle" development system in the new era.The industrial competitiveness between regions is the main bearing area under the cycle system.The comparative study of the development of the competitiveness of the cultural and tourism industry in the region is also the breakthrough to solve this problem.Firstly,based on the data of 11 provinces(cities)in the Yangtze River economic belt and 9 provinces(regions)in the Yellow River Basin from 2009 to 2019,this paper constructs the evaluation index system of cultural tourism industry competitiveness from three aspects: basic competitiveness,environmental competitiveness,and potential competitiveness,measures and compares the cultural tourism industry competitiveness of the two basins with entropy method and analyzes its driving factors with geographic detector method.Then,because of the slow convergence speed of the traditional BP neural network in the prediction task,And easy to fall into local minimum.Combining genetic algorithm(GA)with BP neural network,this paper constructs GA-BP model to predict the competitiveness of cultural and tourism industry in the two watersheds and compares it with a single BP neural network prediction model.The conclusions are as follows:(1)11 provinces(cities)in the Yangtze River economic belt and 9 provinces(regions)in the Yellow River basin can be divided into four categories according to the competitiveness evaluation level and competition situation.The first category is Jiangsu and Zhejiang in the Yangtze River economic belt and Shandong,Sichuan,and Henan in the Yellow River Basin.The evaluation level and competition situation are A and strong respectively;the second category includes Sichuan,Hunan,Hubei,Anhui,and Shanghai in the Yangtze River economic belt and Shaanxi,Inner Mongolia,and Shanxi in the Yellow River Basin.It represents the general level of cultural and tourism industry competitiveness in the two basins.The evaluation level and competition situations are B and strong respectively;the third category is Yunnan,Jiangxi,and Guizhou in the Yangtze River economic belt and Gansu in the Yellow River Basin.The evaluation level and competition situation are C and "general" respectively;the fourth category includes Chongqing in the Yangtze River economic belt and Ningxia and Qinghai in the Yellow River Basin.The evaluation level and competition situation are D and weak respectively;the overall competitiveness of the cultural tourism industry in the two river basins is high,the evaluation level is B,and the competition situation is "strong".In the Yangtze River economic belt,except Shanghai,Jiangsu,and Zhejiang,the competitiveness of the cultural tourism industry in other provinces and cities has been improved;there are great differences in the comprehensive index of cultural tourism industry competitiveness among 9 provinces and regions.The competitiveness score of the cultural tourism industry in Shandong Province is the highest,but then there is a downward trend.The competitiveness score of the cultural tourism industry in Qinghai is the lowest,but there is a slight growth phenomenon.The competitiveness of cultural tourism industry in Sichuan,Henan,Ningxia,and Gansu has been steadily improved,and the competitiveness of the cultural tourism industry in Inner Mongolia and Shaanxi has been improved after 2009,the competitiveness of Shanxi’s cultural tourism industry showed a downward trend after 2009.(2)The comparative analysis shows that the competitiveness of the cultural tourism industry in the two basins is largely determined by its basic competitiveness and potential competitiveness,followed by the influence of environmental competitiveness;in 2009,among the four influencing factors of basic competitiveness,cultural tourism resources ranked first.Among the three influencing factors of potential competitiveness,the influence of industrial-scale is the most significant;The difference is that the impact of market demand on the environmental competitiveness of the Yangtze River Economic Belt in 2009 is greater than that of the ecological environment,which is just opposite to the result of the Yellow River Basin in 2009;In 2019,the influence of cultural tourism resources is the most significant,and the influence of market demand is stronger than that of the ecological environment.The difference is that the influence of industrial-scale ranks last in the potential competitiveness of the Yangtze River economic belt,while the influence of industrial scale ranks first in the Yellow River Basin.Moreover,the competitiveness level of the cultural tourism industry in the two watersheds is not caused by a single factor,but the result of multi-factor synergy,and the interaction between multiple influencing factors is stronger than that of a single influencing factor.(3)In this paper,Shanghai,Zhejiang,Anhui,Jiangxi,Hubei,Hunan,Chongqing,Sichuan,and Yunnan in the Yangtze River economic belt are selected as the training samples of GA-BP neural network prediction model of cultural tourism industry competitiveness,Guizhou,and Jiangsu as the test samples,Shanxi,Inner Mongolia,Shandong,Henan,Sichuan,Shaanxi,and Gansu in the Yellow River Basin as the training samples,Qinghai and Ningxia as the test samples The error values of the training samples are within the acceptable range,which shows that the model can effectively approach the test samples,and the GA-BP neural network prediction model of the competitiveness of the cultural tourism industry in the two watersheds is successfully constructed.(4)Compared with the traditional bp-2 neural network,the optimization of the weights of the bp-2 neural network is more effective than the measured weights,which can alleviate the problem of the minimum of the bp-2 neural network,It has strong applicability to the evaluation and prediction of the competitiveness of cultural tourism industry in other provinces and cities;Compared with the traditional BP neural network,the average relative error of the test set is reduced from 19.35% to 3.91%.In future research,we can easily update relevant data and carry out prediction research on the competitiveness of the regional cultural tourism industry according to the comprehensive development advantages of the regional cultural tourism industry.More importantly,all regions can analyze the problems existing in the development of their cultural tourism industry according to the prediction model,to focus on improving the competitiveness of the regional cultural tourism industry.The study provides strong empirical evidence for the provinces(cities)in the two river basins to improve the competitiveness of the cultural and tourism industry.At the same time,it also provides corresponding management enlightenment for formulating differentiated policies based on accurately grasping the evolution law of the competitiveness of the cultural and tourism industry. |
