Abstract: Spectrum sensing is a foundation in the cognitive radio technology. Normally, due to the hardware limitation,it's the high sampling rate, which is needed in signal sampling ,especially in the case of wideband signals ,that is more and more difficult to realize .Taking both the characteristics in the spectrum distribution and the request for spectrum sensing into consideration, this paper proposes a new spectrum sensing algorithm for the wideband cognitive radio based on Differential Signal Compressed Sensing (DSCS ) by the use of the sparsity of spectrum.Based on energy detection technology,the theory of compressed sensing (CS) is used to acquire signals at reduced rates,rather than the classical Shannon-Nyquist rate. To reduce the complexity,as well as to improve the sensing performance,differential signals,instead of normal ones,are taken to acquire spectrum informations. What's more ,a new tolerance limitation is proposed to halt the iterations for a further reducation in complexity. This new limitation makes the algorithm much easier to match the practical requests-both in time and in computation. Related experiments demonstrate that with a proper tolerance , the wide spectrum sensing methods for Wideband Cognitive Radio based on DSCS leads to a great reduce in both iteration and computation, in addition to a better sensing performance.