A flotation process concentrate grade prediction method based on the Improved Sparrow Search Algorithm （ISSA） optimized Hybrid Kernel Least Squares Support Vector Machine （HKLSSVM）， a flotation process concentrate grade prediction method is proposed to address the issues of delayed variables， coupling characteristics， and limited modeling sample size in the flotation process， which make it difficult to accurately predict the concentrate grade. Firstly， collect data from the flotation site current carrying X-ray fluorescence grade analyzer as modeling variables and preprocess them to establish a prediction model based on the Least Squares Vector Machine. On this basis， a new mixed kernel function is constructed to map the input space to the high-dimensional feature space. Then， an Improved Sparrow Search Algorithm is introduced to optimize the model parameters， and an ISSA-HKLSSVM method is proposed to achieve concentrate grade prediction. Finally， a flotation concentrate grade prediction system based on LabVIEW is developed to verify the proposed method in practice. The experimental results show that the proposed method has a better fitting ability for small sample modeling in the flotation process. It can improve prediction accuracy compared to existing methods， and can achieve accurate online prediction of concentrate grade， providing real-time and reliable concentrate grade feedback information for intelligent control of the flotation process.