官方word2vec的github下载地址：https://github.com/svn2github/word2vec

环境，linux-ubuntu-14.04LST，安装好git， gcc版本4.8.4

linux下的安装方式：

% git clone https://github.com/svn2github/word2vec.git

% cd word2vec

% make

命令解析：

-train <file>
　　Use text data from <file> to train the model
-output <file>
　　Use <file> to save the resulting word vectors / word clusters
-size <int>
　　Set size of word vectors; default is 100
-window <int>
　　Set max skip length between words; default is 5
-sample <float>
　　Set threshold for occurrence of words. Those that appear with higher frequency in the training data
　　will be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)
-hs <int>
　　Use Hierarchical Softmax; default is 0 (not used)
-negative <int>
　　Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)
-threads <int>
　　Use <int> threads (default 12)
-iter <int>
　　Run more training iterations (default 5)
-min-count <int>
　　This will discard words that appear less than <int> times; default is 5
-alpha <float>
　　Set the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW
-classes <int>
　　Output word classes rather than word vectors; default number of classes is 0 (vectors are written)
-debug <int>
　　Set the debug mode (default = 2 = more info during training)
-binary <int>
　　Save the resulting vectors in binary moded; default is 0 (off)
-save-vocab <file>
　　The vocabulary will be saved to <file>
-read-vocab <file>
　　The vocabulary will be read from <file>, not constructed from the training data
-cbow <int>
　　Use the continuous bag of words model; default is 1 (use 0 for skip-gram model)

之后准备训练预料就可以了，将分词后的文件拼成一行，训练即可，

./word2vec -train fudan_corpus_final -output fudan_100_skip.bin -cbow 0 -size 100 -windows 10 -negative 5 -hs 0 -binary 1 -sample 1e-4 -threads 20 -iter 15

对于生成 “fudan_100_skip.bin” 文件，可以用gensim 转换为txt明文形式：

from gensim.models import word2vec

model = word2vec.Word2Vec.load_word2vec_format('path/to/GoogleNews-vectors-negative300.bin', binary=True)

model.save_word2vec_format('path/to/GoogleNews-vectors-negative300.txt', binary=False)

注意：windows下需要先 切换到 gensim的环境（activate gensim），然后再执行

但是以上关于gensim读取的在我这有问题，因此采用原生方法：参考自http://*.com/questions/27324292/convert-word2vec-bin-file-to-text
将以上链接中的c代码copy下来，取名readbin.c
编译readbin.c文件时由于涉及math库，因此命令为：
% gcc -o readbin readbin.c -lm
之后执行将bin文件转换为txt文件的操作即可：
% ./readbin fudan_100_skip.bin fudan_100.txt