Cognitive Diagnosis of Cultural and Rural Tourism Convergence

Abstract Neural networks are widely used in the field of cognitive diagnosis. Cognitive diagnosis can diagnose the subjects’ knowledge of cognitive attributes according to their responses, so as to obtain the specific cognitive status of the subjects and provide remedial measures. The studies on the convergence of cultural industry and tourism industry are emerging, but the theoretical system needs to be improved. The research on the convergence mechanism between cultural industry and tourism industry can complement each other on the basis of independent theoretical system, which establishes relationship between the two theoretical systems. Based on the adaptive neural network algorithm and from the perspective of blockchain, this study takes cultural industry and rural tourism industry as examples to diagnose the industry convergence of rural cultural industry and rural tourism industry development, which will further consolidate the theoretical basis for the convergence and development of tourism industry and cultural industry, as well as contribute to promoting development of industry convergence.

get richer product choices and higher levels of spiritual and cultural needs [1] .
Therefore, based on the adaptive neural Abstract Neural networks are widely used in the field of cognitive diagnosis. Cognitive diagnosis can diagnose the subjects' knowledge of cognitive attributes according to their responses, so as to obtain the specific cognitive status of the subjects and provide remedial measures. The studies on the convergence of cultural industry and tourism industry are emerging, but the theoretical system needs to be improved. The research on the convergence mechanism between cultural industry and tourism industry can complement each other on the basis of independent theoretical system, which establishes relationship between the two theoretical systems. Based on the adaptive neural network algorithm and from the perspective of blockchain, this study takes cultural industry and rural tourism industry as examples to diagnose the industry convergence of rural cultural industry and rural tourism industry development, which will further consolidate the theoretical basis for the convergence and development of tourism industry and cultural industry, as well as contribute to promoting development of industry convergence.
industry and cultural industry are still in the embryonic stage. Industry convergence refers to the russification of industry boundary on the basis of technological convergence and digital convergence. Natural tourism resources and humanistic tourism resources in rural areas constitute the basis for the development of tourism industry. By certain production technological means, these tourism resources are transformed into tourism products and services that are put into the tourism market through operation means and sales channels [2] . According to the classification principle of industry boundary, this study summarizes the industry boundary of rural tourism industry as shown in Figure 1. This mainly includes two stages. The first stage is to use the improved genetic algorithm to search the optimal solution globally. The second stage is to use the optimal solution obtained in the genetic algorithm as the initial value of the neural network, and to use the neural network to search the local optimal solution. The second stage is still composed of three-layer neural networks, and the algorithm steps are similar to those of the standard neural network [4] . The basic steps of the adaptive neural network algorithm are as follows:

Coding scheme
This mainly involves binary coding and real number encoding. The coding of the n th neuron can be expressed by the real number encoding, as shown in Formula 1.

Rural tourism resources
Cultural industry resources     Where, 1 2 , , is the output weight of the corresponding neuron, 1 2 , , k k k q c c c L is threshold of the output layer. The determination of the fitness function decides whether the network algorithm can find better results, which can determine the fitness function, as shown in Formula 2 [5] .

Group setting and initialization and determination of fitness function
Where, mj Y represents the expected value of the j th output node of the n th training sample; ' mj Y is the actual value of the j th output node of the n th training sample.

Operation of network algorithms
In the adaptive neural network algorithm, fitness is the degree reflecting the fitness of the individual to the environment. Thus, so in the evolution, the average fitness and the maximum fitness of the population can be used to measure the condition of the current genetic operation, that is, if the average fitness of the next generation population is lower than that of the previous generation population, it shows that to a certain extent, the evolutionary operation of the algorithm generally evolves towards a method that is not conducive to the population, whereas the evolutionary operation is suitable for the development direction of the population [6] . The adaptive genetic operator control formula is shown in Where,

Analysis of tourism demand in Enshi city
For rural tourism, the material and intangible cultures such as cultural relics, human landscapes, production and life styles, folk customs and traditional festivals make rural tourism distinctive and dynamic, and the development of rural tourism promotes the inheritance and exchange of traditional national cultures in turn. Therefore, culture and rural tourism are inseparable, and complement each other [8] . The number of tourists and tourism income is one of the three aspects that support the development of Enshi tourism. The basic situation is shown in Figure 5.
As shown in Figure Table 1 [9] . Similarly, after multi-step iteration, the rural tourism income in 2018-2021 is output, as shown in Figure 6.    Table 2.
As can be seen from