Since the twenty-first century, new things are emerging in the market economy; the Internet is one of them. Internet of things (IoT) is a new thing, which combines computer, Internet, and mobile communication network after the third wave of information industry, the development of huge application prospect, has been listed as one of the five emerging strategic industries. With the rapid development of digital technology and market economy, to accelerate the competition in the market economy, intense competition in IoT industry also exists. Effect of human resource management to a certain extent determines the strength of the market competitiveness of an enterprise; the research about human resource management of the IoT industry is carried out in this paper, by analysis of the basic characteristics of the Internet industry and combining Internet enterprise competitiveness, and it is directly related to human resource of a number of factors and applies the AHP method to establish the Internet enterprise human resources management quality and the effect of the evaluation model. Finally, an IoT enterprise is taken as the study object, the human resources management quality assessment model studied in this paper is used to evaluate enterprise’s human resources management, and its existing human resources management strategy and its optimization are studied, which provide certain theory support to enhance its market competitiveness.

Supply chain finance and logistics activities are developing rapidly. In the economic activities of the tripartite cooperation between financial institutions, logistics enterprises, and loan enterprises, the goods will enter the logistics and supervision links of pledged goods immediately after they are postponed. It is proposed to integrate computer network communication technologies such as the Internet of Things with supply chain finance and logistics supervision to strengthen the information interaction between suppliers, which is widely used in supply chain activities to help realize the Internet finance. An intelligent supply chain supervision can implement monitoring and early warning of the time of the pledged goods in the warehouse, in transit, and during processing. Therefore, this paper proposes an intelligent supply chain supervision model by integrating supply chain finance, logistics, and pledge finance models into an operation management platform to better promote the smooth progress of supply chain finance and logistics supervision activities, which can effectively reduce various external risks, improve operational efficiency, and provide reference for supply chain finance and logistics activities.

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Dimensionality reduction of images with high-dimensional nonlinear structure is the key to improving the recognition rate. Although some traditional algorithms have achieved some results in the process of dimensionality reduction, they also expose their respective defects. In order to achieve the ideal effect of high-dimensional nonlinear image recognition, based on the analysis of the traditional dimensionality reduction algorithm and refining its advantages, an image recognition technology based on the nonlinear dimensionality reduction method is proposed. As an effective nonlinear feature extraction method, the nonlinear dimensionality reduction method can find the nonlinear structure of datasets and maintain the intrinsic structure of data. Applying the nonlinear dimensionality reduction method to image recognition is to divide the input image into blocks, take it as a dataset in high-dimensional space, reduce the dimension of its structure, and obtain the low-dimensional expression vector of its eigenstructure so that the problem of image recognition can be carried out in a lower dimension. Thus, the computational complexity can be reduced, the recognition accuracy can be improved, and it is convenient for further processing such as image recognition and search. The defects of traditional algorithms are solved, and the commodity price recognition and simulation experiments are carried out, which verifies the feasibility of image recognition technology based on the nonlinear dimensionality reduction method in commodity price recognition.