With the arrival of the big data era, the demand for massive data storage is growing, and distributed storage systems have become a key technology to solve this problem. The traditional HDFS system has a large storage overhead, this paper in order to improve the storage efficiency of massive data, the introduction of corrective deletion code (RS code) technology, to ensure the reliability of the data at the same time significantly reduce the cost of storage. In order to improve the storage efficiency of massive data, this paper introduces the corrective censoring code (RS code) technology, which ensures the data reliability and significantly reduces the storage cost. In addition, to address the problems of low coding efficiency and high repair overhead in the practical application of RS code, this paper further introduces the local repair code (LRC) technology, which reduces the data repair overhead, and compares and analyzes the application effect of optimization model (RS-LRC-HDFS). The experimental results show that after RS-LRC optimization, the time overhead of the HDFS storage system in the write process and read process is significantly improved by 81.12% and 93.01%, respectively, compared with the pre-optimization period, and the repair time of massive file data is reduced by 87.25%. It can be seen that it provides an efficient and reliable solution for massive data storage.

This paper intends to introduce the multi-intelligence of digital resources in cultural and tourism industry in reinforcement learning. In order to scientifically evaluate digital resource allocation, the index system characterizing resource allocation is constructed using hierarchical analysis. From there, a multi-objective collaborative optimization allocation model of digital resources in cultural and tourism industry based on reinforcement learning and multi-intelligent body system is established. Through empirical analysis, it can be seen that referring to the observation of the development of the comprehensive level of digital resource allocation, there is an imbalance in the development level of N province. The indicator system is refined to consist of 4 guideline level indicators and 26 indicator level indicators. Before and after the multi-objective synergistic optimization, the total amount of digital resource procurement for the cultural and tourism industry in province N was reduced by 460,742 yuan. After optimization, the comprehensive efficiency of resource allocation in area a improves by 0.03136, area b improves by 0.03275, and area h improves by 0.02799. Moreover, all of them tend to be in equilibrium. Therefore, the multi-objective synergistic optimization allocation model in this paper can improve the efficiency of digital resources in cultural tourism industry and reduce the differences between districts and counties.

Based on the Delphi method and relevant definitions, this paper determines the evaluation index system of college students’ employability, adopts the hierarchical analysis algorithm (AHP) to calculate the weights of the evaluation indexes, and for the weights of the evaluation indexes do not satisfy the consistency test, adopts the Adaptive Gradient Algorithm (AdaGrad) to adjust the weight parameters so as to make them satisfy the consistency test, and arrives at the adjusted values of the evaluation indexes weights. The weights of the adjusted evaluation indexes are derived. Using the fuzzy comprehensive evaluation theory, a comprehensive assessment model of college students’ completion ability was constructed, and then the research sample was evaluated and analyzed with the help of this model. It is calculated that the affiliation vector of the evaluation of college students’ employability is (1.9466, 1.2539, 1.1123, 0.9752, 4.714), and the maximum affiliation value is 4.714, which can be inferred that the students of this university have good comprehensive ability of employment and can well face the employment pressure in the current society.

Micro-landscape is a kind of green landscape designed to enhance the local landscape environment of the city along with the renewal of urban green space and the transformation of old city. The article adopts Hadoop technology and utilizes the Hadoop distributed computing framework to preprocess the data, constructs the urban micro-landscape greening evaluation system, and carries out research on four evaluation levels, namely, building façade landscape, multimedia landscape, water landscape, and landscape facilities. At the same time, based on the principal component analysis and factor analysis method for comprehensive evaluation, it is determined that the multimedia interaction factor is the most important factor affecting the effectiveness of micro-landscape greening. Then use SWMM model to design a city urban area, through SWMM model simulation to get the actual average annual runoff control rate of the demonstration area in 2023 is 59%, and the overall long-term goal of urban micro-landscape greening planning in 2020-2030 there is a gap, based on which put forward the urban micro-landscape greening design program.

The basic genetic algorithm suffers from problems such as precocity and low search efficiency when solving multi-objective optimization problems in large-scale computing environments. Aiming at these problems, this paper introduces various improvement strategies such as neighborhood operation, adaptive strategy, chaos optimization and cooling into the classical genetic algorithm, and designs an improved genetic algorithm process that organically combines various improvement strategies. The improved genetic algorithm and other existing large-scale multi-objective optimization algorithms are tested using LSMOP test problems, and the improved genetic algorithm has better convergence and diversity than other algorithms on both two-objective and three-objective LSMOP test problems. The PF curves of the seven algorithms are plotted separately for the two-objective on LSMOP6 and the three-objective on LSMOP5 when the decision variable is 200, and the images show that the improved genetic algorithm has the most uniform population distribution. The experimental results confirm the effectiveness of the improved genetic algorithm in solving large-scale multi-objective optimization problems.

With the development of virtual reality and computer vision technology, the demand for virtual scenes of music performances is becoming more and more prosperous, which brings new development opportunities for music performances and music teaching. In this paper, we use the beam leveling method to determine the camera parameters in the virtual scene, implement the calibration process and parameter solving for the camera, and implement the segmentation process for the virtual scene image through the GrabCut algorithm, formulate the model constraints and objective function, construct a virtual scene for music performance, and design a virtual scene system for music performance. Based on the virtual scene of music performance, the interactive learning model of music is proposed, and the virtual roaming mode is formulated by combining human-computer interaction technology to realize the interactive learning roaming of music learners in the virtual scene of music performance. The PSNR and SSIM values of the music performance virtual scene constructed by this paper’s technology are 25.8291db and 0.9396 respectively, which are higher than those of the virtual scene construction algorithms such as VSRS and JTDI as a comparison. Carrying out music teaching experiments, the experimental class that applies the interactive learning model of this paper for music interactive learning roaming has higher mean values of all dimensions than the control class in both music learning ability and music listening ability, showing significant differences (P<0.05).

Text is the carrier of language, and language is the carrier of cultural soft power, if you want a country’s soft power to be enhanced, it will certainly start from the dissemination of the native language. This paper constructs a complex social network J-SEVIR model for the dissemination of Japanese text information with the help of complex network theory combined with the information dissemination model using graph theory as the technical support. The data about Japanese text information on Sina Weibo is used as the research object, and the data analysis is carried out through the dimensions of model simulation, real data comparison, and information dissemination enhancement strategies. The study shows that the peak number of Japanese text message dissemination nodes is 1.987*107, which is 41.32% and 28.94% higher than the peak number of dissemination nodes in the traditional SEIR model and BCIR model, respectively, and the peak number of disseminators of the Japanese text message dissemination enhancement strategy designed by the J-SEVIR model can be up to 0.62, whereas the number of Japanese text message dissemination counterattackers is only 0.12. Therefore, the number of Japanese text message dissemination counterattackers is only 0.12. Therefore, the establishment of Japanese text information dissemination paths with the help of complex networks based on graph theory can be used to provide new research perspectives for optimizing the effect of Japanese text information dissemination.

The prediction of the scale of big data talent training in colleges and universities belongs to an important content in the field of big data talent research in colleges and universities. The article uses the primary exponential smoothing method in the time series and the gray model prediction method to predict the scale of college big data talent training and talent demand respectively, and then uses the Lorenz curve and the Gini coefficient to study the matching degree of education in the field of big data. There are experimental results can be obtained, the degree of matching between the professional settings of colleges and universities and the trend of the demand for big data-related positions in enterprises needs to be strengthened, in order to adapt to the future demand for big data-related positions in enterprises, and to further output talents that are in line with the enterprises, the article proposes a model of big data talent cultivation civic and political education in colleges and universities based on the KSAO model. Based on the KSAO model, the ideological education mode of big data talent cultivation in colleges and universities can be implemented at six levels: “theory + project” curriculum system, promoting the dual strategy of “on-campus simulation + off-campus practice”, establishing the KSAO multi-dimensional practice assessment system, strengthening the coordination of the industry-teaching cooperation model, building a cloud learning platform with the help of information technology, and implementing the top-down education design.

This study focuses on library data mining scenarios and proposes an optimization method for the deficiencies of existing knowledge discovery algorithms in terms of efficiency, accuracy and interpretability. The method first uses principal component analysis to downscale library high-dimensional data to extract the main features and improve the data mining efficiency. Then, the fuzzy clustering algorithm is used to cluster the dimensionality reduced data to more accurately identify the user groups, resource categories and other implicit knowledge. The clustering results are interpreted and analyzed to provide data support for knowledge discovery in library data mining. The algorithm in this paper demonstrates better performance in data dimensionality reduction at the level of memory usage as well as time consumption, and identifies three major components with cumulative contribution of more than 80%. In addition, the algorithm achieves an average purity of 95.45% for book data clustering and a clustering time consumption of 3.47s with a data stream of 300unit k, both of which are better than the comparison algorithms. The comprehensiveness weight of a university’s book resources is 0.17, which is the highest performance, while the practicality and standardization are the next highest, 0.155 and 0.152, respectively. It can be seen from the clustering that the book category with the highest borrowing rate is science and technology, and the lowest one is literature, which reflects the user’s demand for knowledge of a specific field.

Under the impetus of computer technology, the creation of digital art continues to develop, and computer-assisted creation has gradually become the mainstream of artistic creation. This paper is oriented to digital art innovation, in-depth exploration of computer-assisted art creation and its integration with the development of digital media. Through the in-depth analysis of computer-assisted art creation, this paper constructs an improved CycleGAN art pattern generation model by introducing the self-attention mechanism in the CycleGAN model on the basis of pattern generation. In the generation experiments of the improved CycleGAN model, the SSIM and PSMR values of the improved model in this paper are 0.721 and 17.563, and in the number of in-parameters, the model size, and the running speed are reduced compared with the traditional model, and the overall performance of the improved model is excellent. At the same time, the works based on the computer-aided art creation method of this paper compared with the traditional art creation works of the comprehensive average score increased by 11.40 points, further illustrating the more advantageous in computer-aided art creation. The study concludes by analyzing the path of the combination of computer-aided and digital media, and proposes a path for the integration and development of the two from multiple perspectives, which provides directions and ideas for the research on the integration and development of computer-aided and digital media technologies.