The RFID threedimensional localization algorithm is the main technology of indoor localization. Aiming at the problems of low location accuracy and poor adaptability in the traditional LANDMARC localization algorithm, a RFID 3DLANDMARC localization algorithm based on the cultural double quantum particle swarm optimization is proposed. Firstly, the advantages of the BP neural network in data fitting is used to preprocess the acquired signal and the wireless signal transmission loss model is studied to improve localization accuracy of LANDMARC algorithm. With the purpose of solving the adaptive problem existed in LANDMARC localization algorithm, the cultural double quantum particle swarm optimization (CDQPSO) algorithm is introduced, which has the technology advantages in global search and optimization to solve the localization model. The experimental results show that the proposed algorithm improves the localization accuracy and adaptability significantly, compared with the basic LANDMARC algorithm and particle swarm optimization LANDMARC algorithm, with the localization error of 75% of tested label is less than 0.56 m, and it can overcome the shortcoming of slow convergence existed in particle swarm optimization.