Why the VC dimension to this linear hypothesis equal to 3?
The VC dimension for the classifier depends on the dimension of space that your data points belong to.

In your problem, if your mean $x\in \mathbb{R^2}$, then the VC dimension is 3. For $\mathbb{R^2}$, you can always shatter any three general position points ("general position" means they do not coincidentally lie on the same line). For instance, consider three points (0,0), (0,1), (1,0). No matter how you assign labels to them, you can always seperate them by a line. If you consider point points with label (0,0), (0,1), (1,0), (1,1) with label [+,-,-,+], this is the "xor" case which cannot be seperated by a line.

For $x\in \mathbb{R}$, then the VC dimension is 2 because you cannot separate +-+.

In general for $x\in \mathbb{R^d}$, the VC dimension for a linear classifier is $d+1$.