Laser-induced breakdown spectroscopy (LIBS) is a valuable technique for elemental analysis from a laser-induced plasma. The Zhurong rover in the Tianwen-1 Mars exploration mission carries a payload named Mars Surface Composition Detector (MarSCoDe), which can obtain geochemical compositions on Mars. However, the interpretation of MarSCoDe-LIBS spectra will be affected by the complex environment and rock types. With an intent to acquire accurate chemical compositions on Mars using MarSCoDe-LIBS spectra, this work evaluates the performance of several algorithms using the independent third-party LIBS spectral library. This work uses 351 Martian Analogues Library (MAL) to build the LIBS spectral library in a simulated Martian environment. Several models are built based on the LIBS spectra and chemical compositions using nine different algorithms, including machine learning, integrated learning, and deep learning, to derive the major elements (SiO2, TiO2, Al2O3, Fe2O3T, MgO, CaO, Na2O, and K2O). The parameters of these models are confirmed using the cross-validation method, and the performance of these models is evaluated using the RMSE values of the test set. The training set and test set for most models have similar RMSE values except for the ordinary least square method, suggesting no obvious over fitting for these models. In addition, the MLP and GBR models perform better for major elements. Moreover, the RMSE values of the models are similar to those of the published models for ChemCam and SuperCam, suggesting these models have a good performance and can acquire accurate chemical compositions of unknown targets based on their LIBS spectra. This work is valuable for building models suitable for interpreting MarSCoDe-LIBS spectra acquired on Mars.