Tech　mining　is　the　application　of　text　mining　tools　to　science　and　technology　information　resources.　The　ever-increasing　volume　of　scientific　outputs　is　a　boom　to　technological　innovation,　but　it　also　complicates　efforts　to　obtain　useful　and　concise　information　for　problem　solving.　This　challenge　extends　to　tech　mining,　where　the　development　of　techniques　compatible　with　big　data　is　an　urgent　issue.　This　article　introduces　a　semi-supervised　method　for　extracting　layered　technological　information　from　scientific　papers　in　order　to　extend　the　reach　of　tech　mining.　Our　method　starts　with　several　pre-set　seed　patterns　used　to　extract　candidate　phrases　by　matching　the　dependency　tree　of　each　sentence.　Then,　after　a　series　of　judgements,　phrases　are　divided　into　two　categories:　＇main　technique＇　and　＇tech-component＇.　(A　technique,　for　the　purposes　of　this　study,　is　a　method　or　tool　used　in　the　article　being　analysed.)　In　order　to　generate　new　patterns　for　subsequent　iterations,　a　weighted　pattern　learning　method　is　also　adopted.　Finally,　multiple　iterations　of　the　method　are　applied　to　extract　technological　information　from　each　paper.　A　dataset　from　the　field　of　optical　switcher　is　used　to　verify　the　method＇s　effectiveness.　Our　findings　are　that　(1)　by　two　loops　of　extraction　process　in　each　iteration,　our　method　realises　the　layered　technological　information　extraction,　which　contains　the　＇part-whole＇　relationships　between　main　techniques　and　tech-components;　(2)　the　recall　rate　for　main　techniques　is　superior　to　the　baseline　after　iterating　23　rounds;　(3)　when　layering　is　disregarded,　in　the　aspect　of　the　precision　and　the　volume　of　techniques,　the　new　method　is　higher　than　that　for　the　baseline;　and　(4)　adjusting　another　two　parameters　can　optimise　the　efficiency　-　however,　the　effect　is　neither　pronounced　nor　straightforward.