Soil is the material basis of human survival; its characteristics closely relate to peoples production and life. Traditional soil heavy metal detection methods such as atomic absorption spectroscopy and inductively coupled plasma mass spectrometry analysis are weak and expensive, so the development of low-cost operating soil elements quantitative analysis method at the same time. Laser-induced breakdown spectroscopy (LIBS) technology has been widely used because of its rapid and multi-element simultaneous analysis. However, because it is not easy to carry, a split-type field LIBS detector was developed to meet the field testing needs. Its design is to divide the instrument into two parts, probe head, and chassis, and connect it through a plastic pipe. Using a miniature diode pump laser, the pulse energy is up to 100 mJ, with a wavelength of 1 064 nm. The repetition frequency is 1~10 Hz. In addition, using a multichannel high-resolution spectrometer improves LIBSs analytical performance. FPGA is used to realize the us-level delay time function to reduce radiation background interference. To obtain spectral data in 11 soils, The pulse energy was 100 mJ, The delay time was set to 1us, Integration time of 2 ms, Spectra from 10 different positions were collected for each sample, Each position was measured 20 times, A total of 200 spectral data were collected, To reduce the noise interference, The spectral data for each sample were mean-preprocessed after the Beads algorithm baseline correction, The three principal component components with the largest contribution rate were obtained using PCA principal component analysis, In the clustering analysis of 11 different types of soils in different regions by the Kmeans++ algorithm, of the same category of soil into the partial least squares (PLSR) algorithm, Each element selects two characteristic lines and 10 points to enhance the spectral information, One sample was selected as a prediction for quantitative analysis of five soil heavy metal elements, Cu, Cr, Ni, Co, and Cd. the results show that, In contrast to that where no cluster analysis was performed, This method can significantly improve the fitting correlation coefficient of the elements, The correlation coefficients of the five heavy metal elements increased from 0.953, 0.992, 0.989, 0.982, 0.99 to 0.999, 0.998, 0.999 5, 0.996 5, 0.993, respectively, The correlation coefficient of 0.99 and above all meet the requirements of LIBS linear analysis, The average relative error between the prediction results and the actual content increased from 83.45%, 16.03%, 22.94%, 43.91%, 125.768% to 1.14%, 0.99%, 0.895%, 1.879%, 1.862%, respectively, It can be found that after the cluster analysis, Its prediction error is greatly reduced, All were within 5%, With a relatively good analytical performance, The correlation coefficient and prediction error of the five elements are improved compared with the direct PLSR method. Combining PCA and Kmeans++ can be more accurate clustering after dimension reduction, reduce the influence of noise and redundant information, speed up the calculation, reduce the influence of abnormal points on the clustering effect, and improve the robustness.