Feature Information Recognition of Waste Recycling Resource Set Based on Data Mining

Introduction

Waste recycling resources are a series of resource recycling activities produced by various recycling entities in the process of recycling. With the increasingly prominent social problems such as resource shortage and environmental pollution, recycling of renewable resources has become an effective means to develop a recycling economy with its huge economic, environmental and social benefits. However, the development of renewable resources recovery in China is not optimistic (Rachel 2019). At the same time, the lack of scientific management of renewable resources has caused secondary pollution of the environment. As a bottleneck restricting the development of renewable resources recycling industry, waste treatment has been paid more and more attention in practice and theoretical research.

Document (Huang 2019) studied lignocellulosic biomass and its main components (cellulose, hemicellulose and lignin) as raw materials of polymer materials. Thus, the characteristic information of the wood waste recovery resource collection was identified. Based on this basis, the recycling of lignocellulose was studied. The content of cellulose in lignocellulose fiber is high, but in the process of electrospinning, there is a lack of research on the application of electrospinning technology in the preparation of solution from lignocellulose biomass. In this study, ultra-thin (submicron) and nano oriented fibers were prepared by electrospinning (room temperature) with different content of lignocellulose sisal fiber and regenerated polyethylene terephthalate (PET) as raw materials. In document (Ankur 2019), the interaction of Chikungunya virus proteins was studied by analyzing the molecular recognition characteristics, and the transport structure of structural multi proteins to endothelial cells was obtained.

However, the practical applicability of the above methods is poor, which does not involve the algorithm basis and can not provide a reference for the sustainable development of renewable resources. Therefore, based on this, combined with data mining algorithm, this paper optimizes the characteristic information recognition method of waste recycling resource set, and makes a modular analysis of the characteristic factors and influencing factors in the development process of renewable resources recycling, The mutual characteristic mechanism is discussed, and the key characteristic factors are put forward, which provides a theoretical reference for relevant decisions and suggestions. Its innovation lies in the use of data mining method, quantitative analysis of the factors that affect the development of waste recycling resources, access to the causes of renewable resources recycling, impact classification and importance, to further improve the overall operation of the garbage chain supervision system to provide algorithm basis.