Accuracy and real-time measurement of slurry density in recycling box in wet desulphurization pulping system are important for the economic and stable operation of desulphurization process, a prediction model of slurry density in recycling box based on improved sparrow search algorithm optimization (ISSA) least squares support vector machine (LSSVM) is presented. Secondary variables that are highly correlated with the slurry density are selected and preprocessed through mechanism analysis, and use PCA algorithm to reduce dimension. Chaotic mapping and adaptive weights are added to the standard sparrow algorithm (SSA), which improves the uniformity of population distribution and searching ability of the algorithm. It is used to optimize the key parameters of LSSVM and to achieve accurate prediction of serum density. The simulation results of actual data have shown that the average absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of ISSA-LSSVM measurement model are reduced by 44.5%, 43.8%, 43.9% compared with SSA-LSSVM, and the prediction accuracy is significantly better than that of the pre-improvement prediction model, which has some engineering application value.