As　an　effective　tool　for　data　analysis,　formal　concept　analysis　(FCA)　is　widely　used　in　software　engineering　and　machine　learning.　The　construction　of　concept　lattice　is　a　key　step　of　the　FCA.　How　to　effectively　to　update　the　concept　lattice　is　still　an　open,　interesting　and　important　issue.　To　resolve　this　problem,　an　incremental　algorithm　for　concept　lattice　on　image　structure　similarity　(SsimAddExten)　was　presented.　The　proposed　method　mapped　each　knowledge　class　on　the　conceptlattice　into　a　graphic,　when　a　new　object　was　added　or　deleted　in　a　knowledge　class,　the　boundary　profile　of　graphic　will　be　changed,　the　graphic　edge　structure　similarity　was　introduced　as　the　calculation　index　of　the　change　degree　before　and　after　the　knowledge,　and　the　concept　lattice　will　be　updated　on　the　basis　of　the　index.　We　performed　experiments　to　test　SsimAddExtent,　whose　computational　efficiency　obtains　obvious　advantages　over　mainstream　methods　on　almost　all　test　points,　especially　on　the　data　set　with　a　large　number　of　attributes.　But,　its　complexity　is　not　reduced　compared　with　mainstream　methods.　Both　theoretical　analysis　and　performance　test　show　SsimAddExtent　algorithm　is　better　choice　when　we　apply　the　FCA　to　large　scale　data　or　non-sparse　data.