Abstract: To address the limitation of traditional entropy feature extraction algorithms in comprehensively characterizing signal complexity，a feature extraction method called the Rényi complexity-dispersion entropy plane （RCDEP）is proposed in this paper. First，dispersion entropy is redefined within the framework of Rényi entropy. Next，Rényi dispersion entropy is incorporated into the statistical complexity framework，where Rényi complexity is derived by defining the imbalance degree of the probability density distribution and multiplying it with Rényi dispersion entropy. Finally，a two-dimensional feature plane is constructed by combining Rényi complexity and Rényi dispersion entropy. Simulation results demonstrate that the starting point，convergence slope，and endpoint of RCDEP can serve as discriminative features for signals，enabling simultaneous quantification of the randomness of time series and the presence of correlation structures. Experimental analysis reveals distinct RCDEP features among cargo ships，tankers，and tugboats，exhibiting strong separability.