鉴于LCD方法存在的问题，本文提出了一种基于互相关匹配端点延拓局部特征尺度分解（Cross-correlation matching endpoint Extension Local Characteristic scale Decomposition，简称CELCD），由于LCD分解原理是依据信号的局部极值点信息不断进行筛分信号，在信号分解时需要先确定信号的局部极值点，而信号的两个端点可能不是极值点，因此在信号两端点就会出现虚假成分，且该现象随着分解的进行向数据内部扩散，产生端点效应，导致分解结果失真。而CELCD方法的本质在于探索待分解信号前后端的数据规律，延长波形，使得端点效应不出现在原始信号分解的片段上，从而消除端点效应。-Given the LCD method has a problem, this paper presents a cross-correlation based on local feature matching endpoint extension scale decomposition (Cross-correlation matching endpoint Extension Local Characteristic scale Decomposition, referred CELCD), since the LCD decomposition principle is based on the signal of local optima ongoing screening point information signal, when the signal decomposition needed to determine local extreme point of the signal, and the two endpoints signals may not be extreme point, so the two end points of the signal will be fraudulent, and that the phenomenon with decomposition of the data in the internal diffusion, resulting end effect, leading to decomposition distortion. The essence of the method is to explore CELCD data signal to be decomposed before and after the end of the law, to extend the waveform, so the end effect does not appear on the original signal decomposition fragments now to eliminate the end effect.