Heavy metal pollution in soil is caused by human activity factors that bring heavy metals into the soil, resulting in deterioration of soil quality and ecological environment. Heavy metals in the soil tend to accumulate, are difficult to be degraded, are highly concealed for long periods of time, and can be enriched by atmospheric circulation and food chains, ultimately threatening human life and health. Hyperspectral remote sensing technology presents a combination of image and spectrum, and can effectively identify the abnormal conditions of different elements in the soil. At present, traditional soil monitoring techniques mainly rely on laboratory-based chemical detection methods such as photometry, chemical analysis, and atomic fluorescence spectroscopy. This kind of method can test the heavy metal content of farmland soil, but the precision depends on a large amount of manpower, material resources and equipment, and its detection efficiency and promotion are lacking. In order to achieve efficient and accurate monitoring of heavy metals in farmland soils. A method of hyperspectral estimation of heavy metal arsenic (As) content in farmland soils based on particle swarm optimization (PSO) and support vector machine (SVM), which use characteristic-enhanced competitive adaptive reweighted sampling (CARS) was proposed. In the characteristic rough selection stage, the measured spectral values from the darkroom are roughly selected by CARS. In the characteristic improvement stage, First Derivative (FD), Gaussian Filtering (GF), Normalization (N) are used to improve features. In the carefully chosen stage, Pearson Correlation Coefficient (PCC) is used to obtain the correlation coefficient between different pre-treated spectral indices and soil heavy metal As. The band whose correlation coefficient has an absolute value greater than 0.6 is selected as a feature band. Finally, PSO is used to optimize the kernel parameter sigma and the normalization parameter gamma used by the SVM. The root mean square error (RMSE) is used as the fitness function, and the optimal parameters of SVM are obtained by iterating the optimal fitness. The soil of Yanwo Town in Honghu City, a typical area of Jianghan plain, was selected as the research object in this paper. The prediction results showed that the decision coefficient (R2) of the verification sets based on PSO-SVM algorithm is 0.982 3, the root mean square error (RMSE) is 0.521 6, and the mean absolute error (MAE) is 0.416 4. The main conclusions are as follows: the PSO algorithm is used to optimize the SVM parameters, and the global optimal solution can be obtained quickly by iteratively updating the individual extremum and the group extremum. Compared with the support vector machine regression (SVMR) and random forests regression (RFR), the prediction accuracy has been greatly improved; The characteristic enhanced CARS algorithm can effectively eliminate irrelevant information and improve correlation. And it selects fewer bands, simplifies the model so that efficiency is greatly improved; It can realize early warning of soil pollution, meet the needs of precision agriculture and provide data basis for ecological restoration of heavy metal contaminated land in the later period.