Research and Implementation of the Text Matching Algorithm in the Field of Housing Law and Policy Based on Deep Learning

Yin Xu, Hong Ma and Muhammad Javaid

Complexity, 2021, vol. 2021, 1-9

Abstract: Machine learning enables machines to learn rules from a large amount of data input from the outside world through algorithms, so as to identify and judge. It is the main task of the government to further emphasize the importance of improving the housing security mechanism, expand the proportion of affordable housing, increase financial investment, improve the construction quality of affordable housing, and ensure fair distribution. It can be seen that the legal system of housing security is essentially a system to solve the social problems brought by housing marketization, and it is an important part of the whole national housing system. More and more attention has been paid to solving the housing difficulties of low- and middle-income people and establishing a housing security legal system suitable for China’s national conditions and development stage. Aiming at the deep learning problem, a text matching algorithm suitable for the field of housing law and policy is proposed. Classifier based on matching algorithm is a promising classification technology. The research on the legal system of housing security is in the exploratory stage, involving various theoretical and practical research studies. Compare the improved depth learning algorithm with the general algorithm, so as to clearly understand the advantages and disadvantages of the improved depth learning algorithm and depth learning algorithm. This paper introduces the practical application of the deep learning model and fast learning algorithm in detail. Creatively put forward to transform it into an independent public law basis or into an independent savings system.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3165600

DOI: 10.1155/2021/3165600

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