The traditional steady-state visual evoked potential (SSVEP) brain computer interface system usually uses a small number of frequencies for encoding, resulting in a limited number of encodings to dozens, which cannot meet the demands of environmental tasks with a large number of instructions. To address this issue, a Hamming distance multi frequency code (HDMFC) paradigm and corresponding recognition algorithm based on Hamming distance were proposed. The Hamming distance was combined with stimulus paradigm encoding and signal recognition algorithms to encode 120 instructions using 8 frequency signals. Data collection and classification experiments were conducted on 7 subjects. The results show that the accuracy of the 120 encoding online experiment based on Hamming distance can reach 90.60%. The research results provide an effective method for increasing the number of SSVEP paradigm codes and improving classification performance, verifying the practicality and effectiveness of Hamming distance in this field.