In massive MIMO systems， the existing Gauss-Seide （GS） algorithm has lower complexity than the minimum mean-square error （MMSE） algorithm， while the detection performance is worse. This paper proposes a Jacobi-improved Gauss-Seide （JA-IGS） detection algorithm suitable for the uplink detection of massive MIMO systems. The algorithm first optimizes the iterative initial solution by introducing a Jacobi （JA） pre-iterator. Then the traditional GS is optimized linearly. With the increase of lower complexity， the detection performance and convergence speed are significantly improved. Simulation results show that compared with traditional GS and JA detection algorithms， the algorithm has a lower Bit error ratio （BER） and higher computational efficiency.