According to the traditional data mining methods have been mining of low precision

long running time

the wavelet transform and the uncertain fuzzy association rules data text data mining algorithm based on noise

firstly using wavelet transform for uncertain fuzzy data of massive text data noise preprocessing

fuzzy time massive text data sequence conversion to spectrum space

get the distance transform based clustering coefficient

the maximum of the minimum inter class spectrum space

and as a massive text data feature data using fuzzy coefficient

calculate the feature coefficients from membership belonging to the respective categories

determine the fuzzy association rules text data sets

interval division basis the multidimensional degree between the massive data sets

thus the uncertainty of effective mining of massive text data noise. The experimental results show that the proposed algorithm can effectively improve the accuracy of massive text data mining

and the mining efficiency is high.