A Belief Propagation, BP, equalization method, comprising: splitting a received signal Y c, a channel estimation H c and a symbol estimation X c into real parts and imaginary parts to obtain a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X; performing orthogonal triangular, QR, decomposition on the channel estimation matrix H to obtain an equivalent received signal Y bp, an equivalent channel R and a noise power σ 2; and performing iteration based on the equivalent received signal Y bp, the equivalent channel R and the noise power σ 2 to obtain a position probability of per stream symbol; characterized in that: performing iteration based on the equivalent received signal Y bp, the equivalent channel R and the noise power σ 2 to obtain a position probability of per stream symbol comprises: constructing a fully connected BP network by taking the equivalent received signal Y bp as an observation node and the symbol estimation matrix X as an information node, and initializing a per stream symbol position probability as Pro = 1 M ⋅ I M × 2 ⋅ flow, M being an order of quadrature amplitude modulation, QAM; calculating an equivalent received signal mean S u,ONI and an equivalent received signal variance estimate S σ 2, ONI on each observation node index ONI = {2· flow,..., 1}; and calculating a per stream symbol likelihood probability llr ∈ R M× 1 and a per stream belief probability proT ∈ R M× 1 according to the equivalent received signal mean S u,ONI and the equivalent received signal variance estimate S σ 2, ONI. splitting a received signal Y c, a channel estimation H c and a symbol estimation X c into real parts and imaginary parts to obtain a received signal matrix Y, a channel estimation matrix H and a symbol estimation matrix X; performing orthogonal triangular, QR, decomposition on the channel estimation matrix H to obtain an equivalent received signal Y bp, an equivalent channel R and a noise power σ 2; and performing iteration based on the equivalent received signal Y bp, the equivalent channel R and the noise power σ 2 to obtain a position probability of per stream symbol; characterized in that: performing iteration based on the equivalent received signal Y bp, the equivalent channel R and the noise power σ 2 to obtain a position probability of per stream symbol comprises: constructing a fully connected BP network by taking the equivalent received signal Y bp as an observation node and the symbol estimation matrix X as an information node, and initializing a per stream symbol position probability as Pro = 1 M ⋅ I M × 2 ⋅ flow, M being an order of quadrature amplitude modulation, QAM; calculating an equivalent received signal mean S u,ONI and an equivalent received signal variance estimate S σ 2, ONI on each observation node index ONI = {2· flow,..., 1}; and calculating a per stream symbol likelihood probability llr ∈ R M× 1 and a per stream belief probability proT ∈ R M× 1 according to the equivalent received signal mean S u,ONI and the equivalent received signal variance estimate S σ 2, ONI. constructing a fully connected BP network by taking the equivalent received signal Y bp as an observation node and the symbol estimation matrix X as an information node, and initializing a per stream symbol position probability as Pro = 1 M ⋅ I M × 2 ⋅ flow, M being an order of quadrature amplitude modulation, QAM; calculating an equivalent received signal mean S u,ONI and an equivalent received signal variance estimate S σ 2, ONI on each observation node index ONI = {2· flow,..., 1}; and calculating a per stream symbol likelihood probability llr ∈ R M× 1 and a per stream belief probability proT ∈ R M× 1 according to the equivalent received signal mean S u,ONI and the equivalent received signal variance estimate S σ 2, ONI.