The invention discloses a soft measurement method for the burning zone temperature of a rotary kiln. A large quantity of parameter data, including coal gas flow, rod pushing time, kiln head temperature, kiln tail temperature and the like, can be collected on a working site of the lime rotary kiln. However, the data have different influences on the burning zone temperature. According to the soft measurement method, kernel principal component analysis (KPCA) is introduced to process the data, key parameters influencing the burning zone temperature are searched out for modeling, sample dimension is reduced, and modeling difficulty is lowered; a least square support vector machine (LSSVM) is simple in structure, has a complete theoretical basis and only needs few samples, thereby being quite suitable for soft measurement of the burning zone temperature of the rotary kiln; meanwhile, modeling parameters of the least square support vector machine are optimized by a particle swarm optimization (PSO) method, and modeling and predicating accuracy is improved; the soft measurement method is also applicable to other types of rotary kilns.