In　this　paper,　we　propose　a　novel　sensing　matrix　design　for　the　pulse-width　modulation　(PWM)-based　analog-to-information　converter　(AIC),　which　obtains　the　digital　feature　of　an　analog　signal　rather　than　its　sparse　coefficients.　The　method　firstly　selects　feature　subsets　by　feature　selection　algorithm　of　support　vector　machines　(SVMs)　and　then　establishes　the　relationship　between　feature　subsets　and　the　vector　of　sensing　matrix　of　PWM-based　AIC.　Then,　a　sensing　matrix　with　a　higher　compression　ratio　can　be　obtained.　The　new　optimized　sensing　matrix　is　mapped　to　the　reference　modulation　sequence　of　the　PWM-based　AIC＇s　modulation　signal　to　obtain　the　PWM-based　analog-to-feature　converter　(AFC).　Experimental　results　show　that　the　PWM-based　AFC　can　reach　99.40%　accuracy　even　when　the　compression　ratio　is　higher　than　that　of　other　literature.