Perovskite (ABO₃) piezoceramics have been developed for several decades, and there are a lot of data available. It is of great significance to find relationships between structure and properties of materials from these data. In this work, experimental data of Curie temperature (T_c) of BiFeO₃-PbTiO₃-BaTiO₃ solid solution of perovskite piezoelectric ceramics was collected to build the model to predict the T_c. From the perspective of thermodynamics, the quadratic polynomial relationship between T_c and reduced mass was introduced but the deviation was relatively large. More descriptors (including element information, physical quantities, space groups number) and SISSO (Sure Independence Screening and Sparsifying Operator) were used for machine learning to find the correlation between T_c and components. Comparing the root mean square error (RMSE) of different descriptors and dimensions, it's found that more descriptors, more fundamental the descriptors are, and larger dimension will result in smaller RMSE to be used. Meanwhile, RMSE of the same number of descriptors in the same dimension are compared. The optimal four-dimensional model is build using six descriptors: reduced mass, the ratio of A- and B-site ion radii, the ratio of A- and B-site unfilled electrons and element contents of Ba, Pb and Bi. RMSE and maximum absolute error (MaxAE) of our model are 0.59 ℃ and 1.38 ℃, respectively. The average relative error (MRE) of external test is 1.00%. Our results indicate that SISSO machine learning based on limited samples is suitable for the predication of T_c of perovskite piezoelectric ceramics.