Control　chart　patterns　(CCPs)　are　often　used　for　quality　control　in　the　manufacturing　process,　and　effective　recognition　of　these　patterns　is　critical　to　manufacturing.　In　the　dynamic　production　process,　the　raw　data　and　features　of　CCPs　are　used　to　recognize　or　further　predict　the　trends.　However,　the　inaccuracy　of　CCPs　information　extraction,　loss　of　information,　and　complex　recognizer　can　lead　to　the　difficulty　of　recognition.　In　order　to　improve　the　accuracy　of　information　extraction　and　recognition,　a　CCPs　recognition　method　based　on　optimized　deep　belief　network　(DBN)　and　data　information　enhancement　was　proposed.　Adaptive　features　selection　and　information　enhancement　(AFIE)　was　used　to　select　the　most　appropriate　features　and　make　these　features　combine　with　the　raw　data　to　from　the　dataset　in　order　to　reduce　the　data　dimension,　and　then　combine　dimensioned　data　with　the　selected　features　to　enhance　the　data　information.　Further,　this　study　presented　a　DBN　with　three　restricted　Boltzmann　machine　structures,　which　was　optimized　by　using　the　artificial　fish　swarm　algorithm　(AFSA).　The　method　of　AFIE　was　discussed　to　obtain　the　optimal　data　set,　and　parameters　of　the　network　structure　were　analyzed,　optimized,　and　discussed　based　on　experiments　and　AFSA.　At　the　same　time,　this　method　was　compared　with　multi-layer　perceptron　neural　network.　The　simulation　results　showed　that　the　method　proposed　in　this　study　exhibited　excellent　effect,　and　the　recognition　accuracy　achieved　by　this　method　was　99.78%　for　2000　samples　of　each　pattern.