Abstract: Based on BP neural network model technology,a new approach was developed and applied to appraise the input parameters′significant degree through the weightiness and its distribution between the input and output layer.Using the fore-spinning working procedure data gathered from the worsted textiles enterprise,the roving unevenness and weight prediction models were established respectively.The results indicated that the models′mean relative errors are all less than 3%;the correlation coefficientR2between the prediction value and the actual are all more than 0.95.Using the weightiness extracted from the established models,the 13 input parameters′significance to the roving unevenness and weight were calculated respectively,and the remarkable and effective parameters are excavated out.Meanwhile contrasting to the multivariate regression significance analysis(MRSA),the BP neural network method is more exact than MRSA and can be used in the forecast and control of the actual produce and manufacture.