What is the feature matrix in word2vec?
The idea behind word2vec is to represent words by a vector of real numbers of dimension d. Therefore the second matrix is the representation of those words. 

The i-th line of this matrix is the vector representation of the i-th word.

Let's say that in your example you have 5 words : ["Lion", "Cat", "Dog", "Horse", "Mouse"], then the first vector [0,0,0,1,0] means you're considering the word "Horse" and so the representation of "Horse" is [10, 12, 19]. Similarly, [17, 24, 1] is the representation of the word "Lion".

To my knowledege, there are no "human meaning" specifically to each of the numbers in these representations. One number is not representing if the word is a verb or not, an adjective or not... It's just the weights that you change to solve your optimization problem to learn the representation of your words.

This tutorial may help : http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ even though I think the image you put was from this link.

You can also check this, which may help you get started with word vectors with TensorFlow : https://www.tensorflow.org/tutorials/word2vec