## 1 Introduction  
Heavy metal(loid)s are characterized by their accumulation, persistence, and toxicity. Heavy metal(loid)s are easily absorbed by humans and animals through the food chain, jeopardizing the ecology and human health [1]. The sources of heavy metal(loid)s in soil are relatively complex and mainly comprise anthropogenic and natural sources [2]. Anthropogenic sources include mining, smelting, wastewater, exhaust, fertilizers, and pesticides. Natural sources include rock weathering during soil and volcanic eruptions [3]. Qualitative identification of soil heavy metal(loid) pollution and source analysis are important for controlling soil heavy metal(loid) pollution. Timely determination of soil heavy metal(loid) levels and their source pathways is particularly important for preventing and controlling soil heavy metal(loid) pollution.  
Identification of pollution sources is a scientific prerequisite to carry out pollution prevention and control work, and previous researchers have used stable isotope techniques (Pb) [4; 5], new geochemical tracers (Cd) [6], and multivariate statistical analyses (PCA and CA) [7] to qualitatively analyze the sources of heavy metals, whereas the chemical mass balance (CMB) model [8], the absolute principal component score-multivariate linear regression (APCS-MLR) model [9], UNMIX model [10], and Positive Matrix Factorization (PMF) [11] were used for quantitative analysis, and the accuracy of their results can be significantly affected by the number of samples. T-NSE [12], which has higher accuracy and flexibility, uses neural networks to derive t-SNE hyperparameter combinations. Semi-supervised learning, which has the advantages of both supervised and unsupervised learning, can be used mainly for datasets with mainly unlabeled data, and few labeled data and automatic clustering of unlabeled data [13] improves the machine learning prediction performance to a great extent.  
With industrialization and urbanization, soil heavy metal(loid) pollution from industrial parks has become increasingly significant. Different concentrations of Cd, Zn, Pb, and Hg pollution have been reported in various industrial parks [14]. The highly contaminated samples were mainly distributed in enterprise-intensive areas [15].  
The important mineral resource regions in China include Sichuan, Yunnan, and Guizhou. The development of these regional economies has driven heavy metal(loid) pollution by Cd, Zn, Pb, As, and other heavy metal(loid)s [16; 17]. Heavy metal(loid) pollution of soils is more serious in the Yunnan Province, especially in industrial parks where polluting enterprises are concentrated, owing to the early non-standardized treatment of pollutants, lack of a functional layout, and other reasons. This could result from the extensive accumulation of heavy metal(loid)s in the surrounding soil. However, little is known about heavy metal(loid) pollution in the soil of industrial parks. Furthermore, the unknown characteristics of soil heavy metal(loid) pollution, unclear spatial distribution patterns, and unknown sources make it difficult to provide a basis for controlling soil pollution sources and remediating contaminated land in the area.  
This study was conducted in an industrial park in Kunming, Yunnan Province, China. Heavy metal(loid)s in the surface soil (0 to an approximate depth of 20 cm) were studied; evaluation of soil contamination in different land use types using the pollution load index, geoaccumulation index, and Nemero composite index methods; and analysis of heavy metal(loid) sources, which provide theoretical support for the prevention and control of heavy metal(loid) pollution of soil in Kunming City, will inform efforts to enhance regional environmental quality.