Phase space reconstruction plays a pivotal role in calculating features of nonlinear systems. By mapping one-dimensional time series onto a high-dimensional phase space using phase space reconstruction techniques, the dynamical characteristics of nonlinear systems can be revealed. However, existing nonlinear analysis methods are primarily based on phase space reconstruction of single-channel data and cannot directly exploit the rich information contained in multi-channel array data. The reconstructed data matrix exhibits structural similarities with multi-channel array data. The relationship between phase space reconstruction and array data structure, as well as the gain in nonlinear features brought by array data, has not been sufficiently studied. This paper employs two classical nonlinear features: multiscale sample entropy and multiscale permutation entropy. Utilizing array multi-channel data to replace the phase space reconstruction step in algorithms to enhance the algorithmic performance. Initially, the relationship between phase space reconstruction parameters and actual array structures is analyzed, and conversion relationships are established. Then, multiple sets of simulated and real-world array data are used to evaluate the performance of the two entropy algorithms. The results show that substituting array data for phase space reconstruction effectively improves the performance of both entropy algorithms. Specifically, the multiscale sample entropy algorithm, when applied to array data, allows for the differentiation of noisy target signals from background noise at low signal-to-noise ratios. Meanwhile, the multiscale permutation entropy algorithm using array data more accurately reveals the complexity structure of signals at different time scales.