How to use Neural Network classification if data not same size?
You can padd your data. 


  I can not delete or add more arbitaly data. It may make the result not correct.


Don't make assumptions about neural networks. They can map any function, and if you don't add more data (padding) then you can't even train your network in the first place. 

If your data consists of merely 0's and 1's. You could either do the following:


pad all blanks with 0.5 ([0 1 0 1 0, 0.5, 0.5, 0.5, .0.5]), however the neural network will think that 0.5 is as far from 1 as it is from 0. 
pad all blanks with 0.5 (this is what I recommend), if your data is binary (e.g. true/false), then all false must be set to 0 consistently. 0 then has the double meaning: false or not present. 


There is no other option besides adding 'blank' data, if you don't add data, the network will still see this as a 0. 

But you should try before you ask. "It may make the result not correct." is not a good argument to not do something. Your data doesn't seem that big, so you can easily test through trial and error.