随着近代工业的发展，噪音污染已经成为世界范围内的主要环境问题，吸音材料的使用是有效降低噪音污染、静化环境的有效方法之一。熔喷非织造材料因纤维直径小、孔隙率高等特点表现出优异的吸音性能，但其在使用过程中容易受压迫变形造成吸音性能降低。本课题拟利用粗细纤维互相补偿的方式，在普通PP熔喷非织造布中添加高卷曲PET中空短纤维构建插层熔喷双组分吸音材料结构，解决普通PP熔喷材料耐压性差和压缩回弹性小影响吸音性能的问题；利用分形原理对材料结构图象进行二值化处理，研究插层熔喷非织造布结构参数（孔径大小及分布、孔隙率），解决插层双组分熔喷吸音材料结构复杂、难以计算和测量的问题；再利用BP神经网络，建立结构参数（孔径大小及分布、孔隙率、面密度、厚度）与吸音系数之间的关系模型，为设计一种满足质轻、高效吸音材料结构要求的产品及开发高性能的吸音材料提供理论依据。

With the development of modern industy, noise pollution has been the most important environment problems in the world. The use of sound absorption material is one of the effective methods to decrease the noise pollution and keep the environment silent. Because of the fine fiber diameter and the high poresity, meltblowns showes excellent sound absorption performance. But the sound absorption performance is affected by the pressure during its application. In this project, with the method of various fineness fibers blending, crimped polyester hollow staples are added to the normal polypropylene meltblowns to design a new intercalation structure of bicomponent sound absorption material,which can solve the problem of the poor pressure residence and small compression elasticity of the normal polypropylene meltblowns that cause the sound absorption performance decreasing. The fractal theory is use to study the intercalation structure parameters (the size and distribution of the pore in the meltblowns, poresity) of the bicomponent meltblowns, which can solve the problem of the difficulty calculate and measurement for the complex intercalation structure of bicomponent meltblowns. At last BP neural network is used to build the model between the structure parameter (the size and distribution of the pore in the meltblowns, poresity, gram per squre meter, thickness) and sound absorption coefficient, which can meet the structural requirement of the light weigh, high performance sound absorption materials and provide theoretical basis for developing high performance automobile sound absorption materials.