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The sensing performance in existing algorithms is low at low SNR, and is highly influenced by the false-alarm probability. Aiming at this problem, using wishart random matrix theory, a spectrum sensing algorithm based on the distribution characteristics of the maximum eigenvalue of the sampled covariance matrix was proposed. The ratio of maximum eigenvalue and geometric mean eigenvalue is calculated in the algorithm, and no prior knowledges of primary signal are needed. Not sensitive to noise uncertainty, but the algorithm has high detection performance to the related signals and the independent and identically distributed signals. The simulation results show that the proposed algorithm is less affected by the false-alarm probability and high detection performance, and can also get better sensing performance even under the conditions of few number of cooperative users and samples, low SNR and false-alarm probability.