A content-based quantum recommendation algorithm based on quantum Hamming distance is proposed. In the proposed algorithm, quantum mechanical properties are utilized to sum up the attributes of historical movies watched by users parallelly, so that the favorite attributes of the users can be calculated efficiently. Then, the quantum Hamming distance between the new movies’ attributes and the favorite attributes is derived, which represents the similarity of them. Finally, one new movie with the highest similarity is obtained, which means the task of recommendation is achieved. Analysis shows that the proposed algorithm is exponentially faster in the runtime than the classical counterpart.