Decision-Making and Management Method of Public Cultural Service Consumption Preference Based on Multisource Big Data Fusion

Public cultural service is a product of a new era, and it is also an important product of the urbanization process. Public cultural services can not only improve the cohesion between the government and residents, but also it can improve the living standards and happiness index of residents. The public cultural service product is not only a symbol of the city, and it is more important to meet the needs and satisfaction of the residents. It needs to truly understand the preferences and needs of residents and then build public cultural service products according to their preferences. The social public cultural service model under the traditional model is dominated by the willingness of the government, which makes it difficult to truly understand the preferences and needs of residents. This paper uses data fusion and neural network methods to study the influencing factors in public cultural services. The results show that the data fusion method can capture the relationship between the factors of the public cultural service and the residents' preference, and the error of the four factors is less than 2.83% for the classification of the public cultural service. The largest prediction error is only 3.11%, and this part of the error comes from residents' satisfaction with the public cultural industry, which is an acceptable error.